{
 "metadata": {
  "name": "ch2_with_formulas"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "import numpy as np\n",
      "from pandas import *\n",
      "import matplotlib.pyplot as plt\n",
      "import os\n",
      "import statsmodels.api as sm\n",
      "from statsmodels.nonparametric.kde import KDE\n",
      "from statsmodels.nonparametric.smoothers_lowess import lowess\n",
      "from statsmodels.formula.api import logit, glm"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Numeric summaries of height and weight data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, let's import the data and take a look at the first few observations to get a sense of the units. `Height` appears to be in inches, `Weight` in pounds. And `Gender` is a string coded `Male` or `Female`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "heights_weights = read_table('data/01_heights_weights_genders.csv', \n",
      "                             sep = ',', header = 0)\n",
      "print heights_weights.head()\n",
      "print heights_weights['Gender'].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  Gender     Height      Weight\n",
        "0   Male  73.847017  241.893563\n",
        "1   Male  68.781904  162.310473\n",
        "2   Male  74.110105  212.740856\n",
        "3   Male  71.730978  220.042470\n",
        "4   Male  69.881796  206.349801\n",
        "Male      5000\n",
        "Female    5000\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A more detailed summary of the `Height` variable."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "heights = heights_weights['Height']\n",
      "heights.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "count    10000.000000\n",
        "mean        66.367560\n",
        "std          3.847528\n",
        "min         54.263133\n",
        "25%         63.505620\n",
        "50%         66.318070\n",
        "75%         69.174262\n",
        "max         78.998742"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Means, medians, and modes\n",
      "\n",
      "Pretty basic stuff. We'll just make some home-rolled versions of basic mean and median functions, just to follow the text (p. 38)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "def my_mean(x):\n",
      "    return float(np.sum(x)) / len(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "def my_median(x):\n",
      "    '''\n",
      "    Compute the median of a series x.\n",
      "    '''\n",
      "    \n",
      "    # Get a sorted copy of the values in the series (need to call values\n",
      "    # otherwise integer indexing messes things up.)\n",
      "    sorted_x = np.sort(x.values)\n",
      "    if len(x) % 2 == 0:\n",
      "        indices = [0.5 * len(x) - 1, 0.5 * len(x)]\n",
      "        return np.mean(sorted_x[indices])\n",
      "    else:\n",
      "        # Ceil(x) - 1 = Floor(x), but this is to make clear that the -1 is to\n",
      "        # account for 0-based counting.\n",
      "        index = ceil(0.5 * len(x)) - 1\n",
      "        return sorted_x.ix[index]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Check my_mean and my_median against built-ins"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_mean(heights) - heights.mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_median(heights) - heights.median()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Quantiles\n",
      "\n",
      "See p. 40 of the book. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'Range of heights (in.): ', heights.min(), heights.max()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Range of heights (in.):  54.2631333251 78.9987423464\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "# Range = max - min. Note: np.ptp(heights.values) will do the same thing.\n",
      "# HT Nathaniel Smith"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def my_range(s):\n",
      "    '''\n",
      "    Difference between the max and min of an array or Series\n",
      "    '''\n",
      "    return s.max() - s.min()\n",
      "\n",
      "print 'Range width of heights (in.) (my_range()): ', my_range(heights)\n",
      "print 'Range width of heights (in.) (np.ptp()): ',np.ptp(heights)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Range width of heights (in.) (my_range()):  24.7356090213\n",
        "Range width of heights (in.) (np.ptp()):  24.7356090213\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pandas doesn't seem to provide multiple arbitrary quantiles. But (1) the standard ones are available via .describe() and (2) creating one is simple."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To get a single quantile of a pandas `Series`:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "heights.quantile(.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "66.318070081784654"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "A function to get an arbitrary set of quantiles of a pandas `Series`. Give quartiles by default."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def my_quantiles(s, prob = (0.0, 0.25, 0.5, 1.0)):\n",
      "    '''\n",
      "    Calculate quantiles of a series.\n",
      "\n",
      "    Parameters:\n",
      "    -----------\n",
      "    s : a pandas Series \n",
      "    prob : a tuple (or other iterable) of probabilities at \n",
      "           which to compute quantiles. Must be an iterable,\n",
      "           even for a single probability (e.g. prob = (0.50)\n",
      "           not prob = 0.50).\n",
      "\n",
      "    Returns:\n",
      "    --------\n",
      "    A pandas series with the probabilities as an index.\n",
      "    '''\n",
      "    q = [s.quantile(p) for p in prob]\n",
      "    return Series(q, index = prob)\n",
      "\n",
      "# With the default prob argument   \n",
      "my_quantiles(heights)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "0.00    54.263133\n",
        "0.25    63.505620\n",
        "0.50    66.318070\n",
        "1.00    78.998742"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For another example, deciles:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_quantiles(heights, prob = arange(0, 1.1, 0.1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "0.0    54.263133\n",
        "0.1    61.412701\n",
        "0.2    62.859007\n",
        "0.3    64.072407\n",
        "0.4    65.194221\n",
        "0.5    66.318070\n",
        "0.6    67.435374\n",
        "0.7    68.558072\n",
        "0.8    69.811620\n",
        "0.9    71.472149\n",
        "1.0    78.998742"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Standard deviation and variances\n",
      "\n",
      "Again, some home-rolled functions for illustration, checked against the numpy versions."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "def my_var(x):\n",
      "    return np.sum((x - x.mean())**2) / (len(x) - 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_var(heights) - heights.var()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "-2.2106760866336117e-11"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "def my_sd(x):\n",
      "    return np.sqrt(my_var(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "my_sd(heights) - heights.std()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "-2.8728130985200551e-12"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Exploratory Data Visualization"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Histograms\n",
      "\n",
      "We'll plot some historgrams, experimenting with bin width. Matplotlib's `hist` function lets you specify the number of bins, or give an explicit set of bins, but you can't specify just the bin width. So to match the book, we'll create explicit bins with a given width using `np.arange`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 1-inch bins\n",
      "bins1 = np.arange(heights.min(), heights.max(), 1.)\n",
      "heights.hist(bins = bins1, fc = 'steelblue')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 5-inch bins\n",
      "bins5 = np.arange(heights.min(), heights.max(), 5.)\n",
      "heights.hist(bins = bins5, fc = 'steelblue')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# 0.001-inch bins\n",
      "bins001 = np.arange(heights.min(), heights.max(), .001)\n",
      "heights.hist(bins = bins001, fc = 'steelblue')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Kernel density estimators with statsmodels\n",
      "\n",
      "Similar most models in the statsmodels API, we'll compute the KDE by calling it on the data, which produces a `KDE` object. Then we'll call the `fit` method on that object, which will give us a number of attributes, including the `density` attribute, which is the KDE result we're looking for."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "heights_kde = KDE(heights)\n",
      "heights_kde.fit()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the density of the heights. Sort inside the plotting so the lines connect nicely."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()\n",
      "plt.plot(heights_kde.support, heights_kde.density)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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StyGXLl2Ci4sL/QZlQ4YMGQJHR0fk5eWxjkIYoZK3IadOncK4ceNYxyCdiMfj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\n1LQxdq2YPgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We'll pull out male and female heights as separate arrays over which we'll compute densities"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "heights_m = heights[heights_weights['Gender'] == 'Male'].values\n",
      "heights_f = heights[heights_weights['Gender'] == 'Female'].values\n",
      "heights_m_kde = KDE(heights_m)\n",
      "heights_f_kde = KDE(heights_f)\n",
      "heights_m_kde.fit()\n",
      "heights_f_kde.fit()\n",
      "\n",
      "fig = plt.figure()\n",
      "plt.plot(heights_m_kde.support, heights_m_kde.density, label = 'Male')\n",
      "plt.plot(heights_f_kde.support, heights_f_kde.density, label = 'Female')\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ySCQS5W2JRIKSkpa//GQyGYyN/+kINjY2RnFxMTw9PTF9+nSEh4fD398fISEh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\nQtTi/wPYYBiV8l8QzAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And we'll repeat with weights."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weights_m = heights_weights[heights_weights['Gender'] == 'Male']['Weight'].values\n",
      "weights_f = heights_weights[heights_weights['Gender'] == 'Female']['Weight'].values\n",
      "weights_m_kde = KDE(weights_m)\n",
      "weights_f_kde = KDE(weights_f)\n",
      "weights_m_kde.fit()\n",
      "weights_f_kde.fit()\n",
      "\n",
      "\n",
      "fig = plt.figure()\n",
      "plt.plot(weights_m_kde.support, weights_f_kde.density, label = 'Male')\n",
      "plt.plot(weights_f_kde.support, weights_f_kde.density, label = 'Female')\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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XXLlyBc7OzrCyspLJ/pSFs7MzdHR0aBZoNURhQ2TmXk4ZzMU6MNXXZl0KLwbY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\nKGwIIYTwjsKGEEII7yhsCCGE8O7/A00JSO3sXTkvAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Put these in a stacked plot, like in the book."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axes = plt.subplots(nrows = 2, ncols = 1, sharex = True, figsize = (9, 6))\n",
      "plt.subplots_adjust(hspace = 0.1)\n",
      "axes[0].plot(weights_f_kde.support, weights_f_kde.density, label = 'Female')\n",
      "axes[0].xaxis.tick_top()\n",
      "axes[0].legend()\n",
      "axes[1].plot(weights_m_kde.support, weights_f_kde.density, label = 'Male')\n",
      "axes[1].legend()\n",
      "plt.xlabel('Weight (lbs.)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<matplotlib.text.Text at 0x10920cc90>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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Lli06XQ4RkTFj6SIyQSdPnsSECROwe/dudOzYUefLs7S0xJo1azB9+nRUVVXpfHlERMaI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\nEYmApYuIiIhIBCxdRERERCL4HxPLn+dVp/GlAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Scatterplots and lowess smoothing\n",
      "\n",
      "Pull weight (for both sexes) out as a separate array first, like we did with height above, and scatter plot them."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weights = heights_weights['Weight']\n",
      "plt.plot(heights, weights, '.k', mew = 0, alpha = .1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "[<matplotlib.lines.Line2D at 0x125855e10>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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//jzH9pJfTp9O5ubmNCvr6HWcPn2aXz+1NdL7S7cRAb/2iLALNyRqq+Ja3Rpq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\nTkSuQmCC\n"
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Compute the lowess smoother. The `lowess` function in statsmodels is pretty slow. (For a discussion on why, and a faster version, see: <a href = \"http://slendrmeans.wordpress.com/2012/05/14/how-do-you-speed-up-40000-weighted-least-squares-calculations-skip-36000-of-them/\">this blog post</a>.)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lowess_line = lowess(weights, heights)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize = (10, 7))\n",
      "plt.plot(heights, weights, '.', mfc = 'steelblue', mew=0, alpha = .25)\n",
      "plt.plot(lowess_line[:,0], lowess_line[:, 1],  '-', lw = 2,\n",
      "         color = '#461B7E', label = \"Lowess fit\")\n",
      "plt.legend(loc = \"upper left\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "<matplotlib.legend.Legend at 0x109236b50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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nev2THcMmERHNK85ANZ5zs+U5euVRN56zzUWuLm6RK6DpJDjK+KKO/ihiCRMA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\nD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YQQMmuwZnOG+Md//Ef84Ac/AAD09PQgEongIx/5CLZt2wYA2LZtGzZv3jyXSySEEEII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\nAMAmo0/AkUIvVD0AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Logistic regression of sex on height and weight\n",
      "\n",
      "For logistic (and GLM as we'll see below) regression we can use statsmodels's formula interface (using patsy).\n",
      "\n",
      "To do this, lets first make a binary variable, `Male`, which equal 1 if `Gender` is `Male` and 0 otherwise. There are a number of ways to do this, but for fun, and to be explicit, I'll use the `map` method in pandas."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "heights_weights['Male'] = heights_weights['Gender'].map({'Male': 1, 'Female': 0})\n",
      "male_logit = logit(formula = 'Male ~ Height + Weight', df = heights_weights).fit()\n",
      "print male_logit.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Optimization terminated successfully.\n",
        "         Current function value: 2091.297971\n",
        "         Iterations 8\n",
        "                           Logit Regression Results                           \n",
        "==============================================================================\n",
        "Dep. Variable:                   Male   No. Observations:                10000\n",
        "Model:                          Logit   Df Residuals:                     9997\n",
        "Method:                           MLE   Df Model:                            2\n",
        "Date:                Thu, 20 Dec 2012   Pseudo R-squ.:                  0.6983\n",
        "Time:                        14:41:33   Log-Likelihood:                -2091.3\n",
        "converged:                       True   LL-Null:                       -6931.5\n",
        "                                        LLR p-value:                     0.000\n",
        "==============================================================================\n",
        "                 coef    std err          z      P>|z|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "Intercept      0.6925      1.328      0.521      0.602        -1.911     3.296\n",
        "Height        -0.4926      0.029    -17.013      0.000        -0.549    -0.436\n",
        "Weight         0.1983      0.005     38.663      0.000         0.188     0.208\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can do the same thing with the `glm` function, if we specify the binomial family and logit link. This is typically how I'd go about this in R. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "male_glm_logit = glm('Male ~ Height + Weight', df = heights_weights,\n",
      "                     family = sm.families.Binomial(sm.families.links.logit)).fit()\n",
      "print male_glm_logit.summary()     "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                 Generalized Linear Model Regression Results                  \n",
        "==============================================================================\n",
        "Dep. Variable:                   Male   No. Observations:                10000\n",
        "Model:                            GLM   Df Residuals:                     9997\n",
        "Model Family:                Binomial   Df Model:                            2\n",
        "Link Function:                  logit   Scale:                             1.0\n",
        "Method:                          IRLS   Log-Likelihood:                -2091.3\n",
        "Date:                Thu, 20 Dec 2012   Deviance:                       4182.6\n",
        "Time:                        14:41:37   Pearson chi2:                 9.72e+03\n",
        "No. Iterations:                     8                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "------------------------------------------------------------------------------\n",
        "Intercept      0.6925      1.328      0.521      0.602        -1.911     3.296\n",
        "Height        -0.4926      0.029    -17.013      0.000        -0.549    -0.436\n",
        "Weight         0.1983      0.005     38.663      0.000         0.188     0.208\n",
        "==============================================================================\n"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can use the logit parameters to draw a separating line in the (height, weight)-space.\n",
      "The line will separate the space into predicted-male and predicted-female regions."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The calculations below transform the logit model parameters into the slope and intercept of the separating line."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "logit_pars = male_logit.params\n",
      "intercept = -logit_pars['Intercept'] / logit_pars['Weight']\n",
      "slope =  -logit_pars['Height'] / logit_pars['Weight']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Color coding the points by sex in (height-weight)-space, we can see how well the logit classifies sexes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize = (10, 8))\n",
      "# Women points (coral)\n",
      "plt.plot(heights_f, weights_f, '.', label = 'Female', \n",
      "         mfc = 'None', mec='coral', alpha = .4)\n",
      "# Men points (blue)\n",
      "plt.plot(heights_m, weights_m, '.', label = 'Male', \n",
      "         mfc = 'None', mec='steelblue', alpha = .4)\n",
      "# The separating line\n",
      "plt.plot(array([50, 80]), intercept + slope * array([50, 80]), \n",
      "         '-', color = '#461B7E')\n",
      "plt.xlabel('Height (in.)')\n",
      "plt.ylabel('Weight (lbs.)')\n",
      "plt.legend(loc='upper left')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "<matplotlib.legend.Legend at 0x10b11e790>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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4/XS4Hoj7OpzoOrQ5mrhhrZdutw3jzIzOxSysHo9G0hgNKqORNAGPjft3dqED\nqqrWl9jnVjkXVogvtRotlkc+XSGEEOIiXa7GneWE1nP9jsmg0utzLGte5MLX3bzeh8mo4nFYeP7I\nBJ0uK36XjReOTjIez5ItlimWNdB1FEXhtVNTtLdaMRsNtLda+T+vnmFHjxvVYMDrtLB7jZtMocJH\n7trAvdu7aDYZGQwlGYmkMBlV7t/VNW8G5i0bvCgoMLMkv7C5aqnP4ndHJ9h3MkSxXKVY1nj9dJhj\nY3FUpTYOabGGLQmXK0MahIQQQoiLtLBx52JGFM12ewfctZC31Ik9C0+wMRvVefdazrMs9jNFqQ04\n/8n+M2zpdpEvVfnefx5na7eLiqZxejJJIlvkT+/ZyGsnp1jb3sJLxycZj2UIuG3sWuMmnCygKPDh\nuzYwGcvy0fU+9p0IoaMTms7jspnoaLVhXqRquSngQtdhMp7FYCjx/NFcfe7muTr3uzx2XLYmMoUy\nJ8YT/PE71nNyYhqDysz7Unj5RLC+T1SsHIn2QgghxEVauCx7MZXOud3eL58InlXZm3sGeDJXqlfq\n5p7a851njzAUTvE/fnOMUkWr77VceKrPYs/X1+FkMp7l5vU+dF3n6Gicte0OqprOls5WUrkSH7pt\nHSPhNBVNJ+C24bI3oQA3rPNyeDTOunYHAbeNV/tDqKrCRCyLjk6Xx86uNR58ThuxTIHNXa6zKrQm\ng8qOXg9mkwFFUfG2WMiXKot+fgs/i2KlSjiZZ8/aNtCpNw5BrVP9gd3drGtvkXFGK0zCphBCCHGR\nTAYVs1GtH/l4ZirFkZEYzx+boNfnWNY1qppGsVTl5weG62eDzzUbEGePV5wNtrOVTFVV6Gt3ssbb\nwl1b/YQSuVq3dyRd//vbN3XM675ebGl5U6CVaKrAB29bRzxTpFiu8uKJIP/XTb2EpnMzAVfjuSMT\nRNMF3n/TGt48E2HPGk8t2M7c5zdvjzIazTAcSXNsLEqxrBFO5rhvewCTQWU0mgGgUKrSP56oB8ix\naAanxcTgVJJDwzGqmr7osZizn0UokcXaZOI9N/Swrcd9Vrf+ueaeiitLwqYQQgixwGwAWs6w8LnV\nQk3TMBpU7tzsZyScXta9+jqcTCXz/Ok9G9nY2XpWFW42NI3HMvP2O/Z1OJmIZ+ny2JlK5nHbmzg0\nHMXXauHF45N0uKyMRtNUKhqvngxR1XR6vI4lRyiZDCodLhvf++0JhsNpYukCd27u4NdvjzI7t6an\nzcH6jhYBpWA9AAAgAElEQVQ+eOs63joT5YFd3Wzr8fD2UJRSReOV/iD2ZjO3b2xnc2crsXSJRKbA\nBn9r/VzzSlUj4LYxHqsNaD8zlWKNz8HWbjej0QzT2RIfuXtDvSI5txJaLFfqn8W79/Swo8c9r9Fn\nNJKu/+7sezWqylnnv4sry/DlL3/5yyv9ENezbDYLgM0me0qEEGKlDYaSxNJFhsJpdq9to1CqVQZ7\nvQ6GZo5rXCiSymNtMjEUTtFibWKtr4XRaJoNna0YVGWRu8xnUBXKVY1oMs+R0QQOixGXvbn+Wqet\niaGpFAZVIZkrE0nlcdqaGI2kcVjMHB2Ns3dnF28MRHjwhh5OTyZ5584uXLYmNB1C0zneubOLeLrA\nsdE4rTYz05kiiWypfq3Ze5UrGslskfV+J6oK+/qn+H8e3M50psBvD08wEc/SajWTLZa5dWM7JoOK\nQVUIpwoMTdUagIbCacxGlVOTSW7b1E6uWOGGPi/RZJ7Do3HKlSpnQkmimSJbulqJpgsMhpKkC2V6\n2+z0eO24bM28fCKI02pmJFL7LhzNZuLZEul8iQ0zYXn2+4qk8sQyRUajWezNRs5MJXlzMEq5qmFr\nNtVODmoyLfkdiuW7mNwiDUJCCCHEjIUnygQTWe7c7D/nyT26Di8en+TubZ2YDOqipwjNbcxRFAVV\nURiJpAm4bfXTdF4+EWTvzgDlijavSai+VL+gicg4U83rnangveeGHgBUVaF/PMFYLMO92wOYDArl\nisZbQxFu6vNRKFcJxnNs7HRSmNnPuXdnV71ZyGlrIlcsk8xWeHB3NycnppmaznNjn5dOt43nj06w\nrsPJaCRdf0/VqsaumeV0t72ZU5NJbtnoY0ePh2pV53++cIp4rsRGfwttLRaefXuUnjYHyWyJYDzH\n7Zs76GmzMxnPoqq1xh5N11nvd1IsV+un+2xe0DQ0t2nq5RNBerx2vC0WfvnmKJ993w4OD8f4zcEx\nqpqOgsLW7uWdo76Yy30+/fVEPikhhBBixsITZe7dHmAynl102Xm2i3y2m3sknD5rD+fssu3cpfaR\ncJoNfiftzlqFbfZnPW12yhWNl08EAeYt4ZcqGgG3lWyxwk9eO0NV0+tLynP3JNYCkc7bQ1G8LRZe\nPDZZX07WgZFImgOnw4zH0qRypXn7OQEUQFV1jowk2Nnr5lQoxUQsQ1Wrkp3TBW9UFfafCjMSTvPq\nySkODsV4pT9IoVzFYTHxyL0bMRlUjo3GmIhniaYL3LetE5etmR+/Moim1+Zz/vyNYTRdo6fNzngs\ng6rAeCxLj9dOW4uFo6NxFAWMBnXR72DuvsxOt6120tGpKTb4HaRyJQ4OR/nku7YyEcswmchc0AzS\nhS73+fTXE1lGX2GyjC6EEKvH7JL1pkArfpcNk6HWHT27zDx32baq6bRYTXhbLOw7FeLm9T5eOjHJ\niYlpCqUKG/ytjEUzeFss85baU/ky+WKFAwNhbuzzMh7LsKGzFbe9maGpFE6rmS1dLrKFcn0J//BI\njC1dbn762hBr21tIZIrE0wV0RZk3IiiWLtJiMZGdOVt8bXsLqXyZbd1ugokck4kcHnsTY7Esh0di\nmI0qVR22dLkYDqc4cCZKi8WMx9FMqVJlY2crPqeFbT0ejo/F8TiacTuaa/sjoxnKVZ1MvkyXx0qm\nWMWgQkerlWSuTLlS5a3BKNt73Ewl89ibjfzHwXHW+x00m0xEU3l29HrYHHBxejLJzRt8JHNl7M1G\nsoUKh4Yi6OjcuN5Hl9u26JaE2e9rQ2crbS3NjETS3LzBxxpfS72ZytdiYSya5f6dXTiazRe9lD73\nO1zuFolr0cXkFqlsCiGEEDPON/R7XoUykq5X0mY7rasa3Liuje09bv799aF6xXF25qOuQ6fbitGg\n8t9uWVs/TWc0kmY4nGYinuXA6TC/fHOE/aem8DktDIVT3L2tk8l4li1dLgIuGx2tVvxuG0ZVmfes\nVU2jWNY4MBBma5eLqqaDrjMYShJO5muVPa0KwK0b2qlW4e3BCMPhNGemUrhsZm5Y5yWcyqPpcGg4\nykAwwcsngpiMKkaDykgkzaHhGFPTOcZjGQqVKgbVQEerBYPBwFis1m1eqepUdY1er4Nt3S5OB1Pc\nusnHnrU+4pk82WIZRVEwGZR649PsCUWHR2L8yd0buW971zkbreZ+X3P/22o2zutS7/JYKVe0S+pM\nP9/59GJp8mkJIYQQyzR32XY2AD6wuxurudYCUa5UabU18eu3Rnn4jr56KFEVhb6OFnRd542BCJ1u\nG6HpHPfv6qoFzUiGqqbhcTTjdVrodNvY3uOuh1Gr2ciWLhddHhtD4RSVqobZqLLe75zXOV+uaBwc\nivDfbu5lcjrHkdEYiqIwNJVCVRQczSaCiTzBRI5yVeN0cJqqrrPG56jNtyxWePH4JHvWeTEZFW5e\n7yOSLqIDvhYLE7Es7U4Lvz08zh/e0cdNfV5MqoKOzrp2J3dt8XNkNM5gKMnrA2EKZY1jY3FszWbW\nttuJpYu8MRDG7bDwjs1+mkwKoelc/fOdO/MTnUseW/T7sU6uSw6KcvrQxVN0fXaggVgJF3OgvRBC\niJVRrtaad9b7F28QyZUqvHB0knu2d9YDKFDbvzkzi7Kj1crrp8P1at7pYHJmJmaZ1wci7F7jwWhQ\nMBkM9Hod85bJT05O89qpKSKpAm67mQ6XFQWFvTu76B9PYDSoFMtV3hyKcGIsweZAK9t73Ozrn6LV\nZsbSZCSWKmJUwWAwsK3HxdR0DrPRAEBF0+hpc7C9x12rglY0xmIZbljn5ZdvjnDDOi9mo0q+VMWg\nKpiNKmvbW3jx2CR+l5XDwzFOBaexNZnp62hBATLFMjf1eZlM5Ohts/PC8SAjkQy3bvDS621hjc9R\nr9ou9TkPhpKMRjNUqtp5TxcSjXUxuUW60YUQQoglzHYgz+0cXypoAvXl24X6Opw8d3gcv8vGgYEw\n3W32+vWHwylyxQqRVIH/9z3b+eHzJ8kUyty6oR2/yzqvM30yniXgseO2NzE1naVa1Tk+Fmc8liWR\nLXL3Fj9Gg4JBUbl/Zxfbe9z84o0RqprOmXCKUlnDaTWzd0eAcDKPQVXo8ToIT+e4f1c35YrGSCTN\naCSNrsMbgxG29rj49Vuj7FrbxqHhCLvWeAkmavM9ZyuFe3d28bujE/jdVgrlKqFknmJZo1Au8+h9\nmylXNA4MRHDZmjCqCh+4uZfnj05Q0XRAZ2u3e97nNVtFnDV7PGXAbePk5PRZx3XWGptU6RRfpSRs\nCiGEEEuY3aNZKFUA6p3Ii51dPtdsJW4inqXN0YyiQMBj463BKA/f2Qc69fFF924P8PTzJ9F0jVdP\nhfA4mnjvDT202pr46WtDvGNLB6dnTv8plKv4W62cSucYDKVxOyxUNJ14usjd2zqYmDlVR1UgmSvy\n09eHubmvjUiqQDCRxWWvzd48Pp7A57RgNhqwmg3EDQaOjcXr44GGw2m2dLnodNv4/nMneHBPD8VK\nFY/DSiyVrw2Sn86xvacWEk0GlZ42OwOhFG57MxazkUyhxG2b2usd9rvXevj3/UPsWOPh8HCMe7cF\nUFUFVZ1/XvpiI4aqmkahVOXERAKjWts+MDzT1Z8rVni5P8gDu7rr58af7/u5UDL26NJI2BRCCHHd\nWW54mN2jORbLnHPe5kKj0QyaXhuSXixXiCQLdLps7Frr4eTENGZjbZTP7FJ1R6uFTredsUiaRLpE\nMlvilf4Qt23qYDyeI+C2UixVCcazHB2JUtFAQ8fntDA1ncfntKBpYFRVetvs3L3Vz7+8cAqzUWU6\nVwal1qm+3t/KgYEwO3s9VKoavzsygdfZjNveRL5Ywd5smmlWSpEtlFEU6rM8T4wniKXyqIrCeDzH\n5k4n/eMJdvR6gFr1diCUIuC2Ek4X2LOuDVVRePH4JAG3beb3FA4OR9F0nYlEjr72FjYHfv95DoaS\nvH46TFuLhUK5zNDMOKn2VkttNJKqsNbXwmAoSaWq/37skcta/+/lfD8Xau48z0aE2WudRHMhhBDX\nneXOTJztQD7XvE1g3pGKs0cyeuxN5EoVylUdl6OJQkVDQSE8nUVVFAZDSaqaPjNXU2FdewvJXIm9\nOwMcG49zwzovVrMRXddJ58v8x+FxbuzzoigGOlqt7F7j5cXjQRQVdF3nlZNBDAr8r1cH+c6vjjIa\nSeN3WZiMZwhPF1jra+H05DRbu9wzn4FOuVqlqmk4rU28eCJIn9+JpsO92wPoQHA6D+j89tAYoHNj\nnxcdnffs6SaYzIPy+/E/JoPKho4WUoUy63wOVEVhS5eLbo+Nt85EOTgcZTyWob2lmS6PnWgyz1g0\nPW/25WgkQ2VmpNRELIdKrelqIpblrq2d9UH2PW0OdF2vN/1cjgagc5Fz1i+NVDaFEEJcd+aGh6VO\nBpqtfM7u0TxXNWs0mqHLY6dQqtI/nsDvspEvVTCqCgG3DbPRgNlkwGxQMBqNKAr4XTZeOzXFA7u7\nSeZK7OsPoeu1AeaqohKazhNLF/C0NBOeznPXFj9j0Qwuu5m1PgfPHZ4g0GYjkSlhNhjw2Jvpn0yy\nrctFPFOku83OWDRHrlTmrs0dvHA8SJvDQjiZI5yqnaVeruqkcmWOjcV59L5NjMyMXyqUKhwejuFz\nNtPdZidf1oimC6iKQovVzEsngqz11aqScz+r8WimFmqD04Sm8xRnguLDd/ZxcmKaLreNwyNx/uDW\ntfzvVwcxqAqFUpXnDo/T1+FkLJZhY6eTkXAGRdHxtVoJJrLsWdvGyyeC9Hrt9e9t84LO8EZWG/s6\nnIueDCWWRz4xIYQQ153zzUzUdFCV2jL4c4fH65W3H78ywL++dJqn/qufZL4E1ILpeCxDtlCmWKnC\nzKD1U8Fperx2Xj05harW5klqOgTcVrwtFl7uD9LptpErVhiPZbh/Vxe+1toeTI+9CbNBod1p4fmj\nk3S0WumfmKZ/IsEGv5P9p6bobrOxxuugxWLm8EiUN89ECSay/K99g6TzJd4eijIWSdNkVHlzMMrm\nQCs39nmJpQpk8mWKZQ2PrZlssYIOvDkQoarVqrLBRI5Wm5mqpuN32QhP5zCo0OWxoVN7LzvXeDAZ\n1HqVuFrVODwa53RwmvFYFo/dTLmi1eZ7BpMEE1k2dbm4sa8WHOOZIl6nlWKlik7tGlu7XUzGczSb\nVT5810ampnPcuqGdN89EuG9H4KIqmAurzhfjXGOPLsf1r3USNoUQQlx3ZsPDaCS9aFCYrXwuPM6x\noul89K4NvGtXF/9n3xmgFkz3rPVSKFeZms7Xz+/uaLXxnj293LXVTyiRW3QQ/KZAK/v6g/Vl9Y5W\nKwBGo4qv1YrZZODj79zMkdE4AbeVrV1uXu2fYmOXi7FYlnypwsmJBPF0gWaTgUKxjNNm5tBwjGyx\nzE0bfHidVibiWXQUXjs9hdPexPtu7GUyngMVHtjVhb/VSk+bnXXtTk6MJ9B0nRMTCcaiGf79tSEC\nbiuT8RwjkTSxVA4U9azPaiyW453bOymUqijodLjsHB2N43M2AxBw23n5eG3LgKrAtm5XfUC8f2bP\npclgYIPfyXtu6MVpMbN3ZxfhZJ4HdtVmmS51HOi5NPqYSTnG8vwkbAohhLiuzK1ElSraWUFhMJRE\n1+E/D42TK1Z4pT+IpmmcDiYJxrMMhVP86q1RPnTHOqAWtrrb7MTShfrsTKjtNYylCxwajnLLBt+8\nQfDrO1qYiGUZDCUJeOz1fYgGVaHZZGDvzi6OjMQolav84o1h2lqaOTQc5+hoHB2dcDyLzWwgnMqz\nKdDKnrVthJNZHNYm8qUq/9/DN1IoVzkxHufQUJQujw2jCm57Ey57M0dGY/R1OJiIZ/jPQ+P0j09T\nKFd5/tgEDquJY6MJdB2GY2m8Tgtr251Uqhrlqs7ONV5UNH53dIL+iUT97PUujxVFUTkTTrG1282R\nkRh//I71BDx2KppOMJHlvh0B1rW3oCgK/lYrzWYDH7ptHQZFYV9/CLNRxWRU69/DcDhd3xY6+72d\nmUqxxudYdrhr9H5L2c95fnI2+gqTs9GFEOLKiqWLbPA7sTaZODwSo9Ntm3fedSxdZFOglWazkXAy\nz52b/ZyYmOaW9T76Olr4ny8N8Og7N+G0mIHa+dyzZ3JPxDL1s9O3dLvZf3KKB2/oIZTIsaGzlYlY\nBkVRGIlk2L22DUezed4zbAq4yBTKHB9LkMwWOTExTTpf4cxUiqlkjmSuRDpfJpYpkMyVOTOZIp0v\nMh7LsDHgolCucvN6L8fHp0nmSjgttSalTL7MzjUe8qUq63wOouki6NBqN7NrjYfRWIZ8qcLa9hZC\niTyFchl7swldgxarmcOjMdAhlStS0aqoqoF37erG0WxmJFIbk5TIlnhrMMLtGztQFIVYKk+zycho\nNEOL1YzL1oTTamYonGL32jaOjyW4c7OfyUSWqek8+09PcXQ0xsBkktPBBOWqzg3rvPXzzBVFYYPf\nSTpfJpzMk86XlnVG+dzz0xux37LR119tLia3SNhcYRI2hRDiyhgMJYmliwyGknidFkajaXavbWMk\nkp4XFCKpPNYmE0dHY9y52c94LIOj2YS92cyBwTA3r/eRKZRx2powqAoGVSGVK5HIlupBytFsZjya\nwee0kClU0NFx25s5MhKnxWommMiSLVTIFss4rWZ+d2SCyXiWqWSOeKZIl8dOW4uFcDLPSDRDwGPj\nzMwy+3Akja3JiMvexGQiy22bOujx2hmYSvHRuzYwHsuRKZSwN5tAUYhnivS0OTg5maLTbSGYyBNO\n5jgdStFsMnBoOIHZaOAjd20gmS1R1apUNJ3pbO11Bwam6Gi1cmQ0QTiZw2Q04Gg2ga5zZDSBw2Ks\nB82qrmO3mAinCmztcmM0qGzv8TAQTOK01YK102IiV6xiazKSzJXZFGhlJJohlSuyrt3B4FSGde1O\nUBSK5SrpfAmDqvD6QJjRSJp4tkCrrWnZR0caVAVvi+W8ofRiNfr6q83F5JZrP4ILIYQQ/H5v3e2b\nOtjXH5p35vjc0NLX4eSnr51BAf7ptycolatAbV4kOvVO8rlLuLPX9rZYODoary+pzt3P98LRCcbj\nWbLFMm0tFo6OxAgmsrx1JkJbSzM7ej10tNooVaqcnpymWKnSP5mkVKrwxkC4VqUsVnE0m3DZmmmx\nmKhqOhPxLK+eDPOBG7t45eQUa3wOVFQUTWFoKkm+WGE6V0LXNMLJIuVqlbu3Bdizto2hcIb33dDN\nxk4nL58IYjQovGt3D2aDAZfdQr5UYbO/lWi6gMVk4H039pLKloikirwxGOXubX7WtTs5cDpMRdNZ\n3+Hk0FCMbrcNk1Gl1+vgtdNTaLo+87lZeWsoNrP1wIFRVTAZVMqV2vGXJ8anazNDgS63DaOhNo9U\nVVVuXOcl4LETcNtpNhmuiyritUIqmytMKptCiGvFbOUwksrXq36ryWzFcjRaW/KePXN74TMbVIVw\nqsADu7sxGlTCyQJ71rYxFs3Q47XjbbGw71QIj6OZRKb22uFwin39IQ6PxImn89y+xY/VbOTtoSjZ\nQrm+VF5rhKkSSeXZtaYNt72ZfLHMcDjN0ZkmIJejmVarmWimiNWkMhrNssHfwmg0wyZ/C1VqDU52\nq5lyuUK5orPG52AiUaDN0cx7buhl38kQKKAqKm5bEy5HEzaLiYDbSjxdZDiS5s3BCN1uG2OxLJoO\nrTYTbruFTKGMptcGpj+4p5sDgxEcFhMGVeXQSAyr2ci92ztpa2kmninSP5EgmSvTYjFxOjiNaWbf\npa3JxH8dmcDSVNtbamsy8fzRSW7f6MPvsrHvVIib1/swqAqdHhvHxuJ0uu24HU1s8DvZ0evB77Ji\nUBUiqdoYJU3TMRlVNgVcGFRl1f+buxbJMvpVSMKmEOJaMXcv5NBUCm+L5bLfY6lwMRhKcmgoxkAo\nRb5UxmVvnhc8ZgeoHx6JsXttG80mQ60BJZLB3mzE77IxEknXn/nU5DQuexPPH5vg/p1d9eqc0WDg\n+FiCvTu7yOTLqAoUy1VOTtaqnH96z0amknkODkWJpguUK1UGp1L0ttk5OBzjpj4fp0NJ1rW3MBHP\nEkzkKJSrtNiacNvNTMTzGA0K+bJG//g02UKZNkczw9Es27pdxHNluj1Wktky3hZL7YQdo0o8W+Kd\n2/0cHolzcCjCiYkEqVyJqqYRTOaxNZmpVmvD209MJDAYFLo8djRdZ217C5PxHF0eGw6LmQMDETZ1\nOtm1po1fvTXK7Zt8+FptvD0Uw2RQCXhs7Or1cGoyicfehMvWVAuFBpXBqRT37+xiOlskXSizrr2F\nbLFCp8vGKydDrGt3MBBKsf/UFG0tzVSrGi57M80mAzvXtGE2qtyxuYOA2zbvu61qOiORDE6LiW09\nnnpV80r8mxPzyTK6EEKIFXMlunKXGjOj6dDVZufOzR2Uq/pZXcqaXhv6fffWTkbC6frPAm4r2WKF\nn+w/Q1XT66N07t7Wyeunwzx853rCyTzdHjt7d3ZhUBXW+hxYzcb6+w0mcrgdTZyeTPIvL57ijcEI\nHnutK/zE+DT2ZhOHR+O894YefvnGMOt8LUzGs9y5xY/fZWU6W+Sd2wPYmpt45N6NdLpsTCVyOCwm\nOt12QtM57tzUTkXTuaWvjSaTiZs3tJHKl4Da3koV+N+vDmMxGVAUlft3Bmg2G+l022l3WpiMp7lr\nq59oOs8Gfyv37+hmMpalUK6ioNDnb2EsluW5oxMcHIrxLy+e5vvP9fPQbWtxWJtI5UpsDrRy91Y/\nGzqcfPc3x7hneyc7ej0oCnS32Unmirx7Vxdj0QyhZB6HxcwbZyLcvN7H8fEE92ztpK+jFZ+zmW3d\nbt65vWved7XULEtNh6lkDoOicHA4tuiIKukEX92ksrnCpLIphLhWXImu3Nml8Lnd47M/HwwlGQil\nSOdL7FnnrT/DYCjJUDhFOl8mninUl2AjqTx+l43nj01yywYf47E0v35rjJOT08RSBbrbbORLVYKJ\nHK/0h4il86TzZdyOZpK5EsPhFCcnpzEZFFS1dq/JeJb37OmpdYynC+xa4+E3B8fpa2/hF2+O0GJr\nJpzMEUkVCCYytLVY6+eOV7QqxVKVN89EmUrmSOdry+stVjM9bXZ2rvEwGE4RSxcpVaq8PRzF57TS\najEzlcpz0zovpapGwG1hOJIhky/T3lqbsXnXlk6GI2lUpTaM/e2hKH3+FmLpIvF0kfZWC8F4jj6/\ng2K5itGokswWOTUxjdloYI3PwdtDUd4cjNA/maS3zUYqX0LTdXq8DsajGW5e78PntLL/5BQ7ej0c\nGo7hbWnGZDRw97ZOXjw2ic9pYXAqRY/XTjxTxKAq9e/jXN95JFmgx2tn1xoP+09OsWEmWJ7v35ws\ns19+sox+FZKwKYS4VlyJrtylwoXT1sRQOE1vm50WaxMvHJtkajpHvlRF1+GGPi/hZB6DQcXvstZf\nMztkvXb+eIVtPW6CiSyZQoXRaJajwzHaWizYLSamknlsZgNnpmpVwjOhFLliheFIhnAqR4u1ie42\n20xITHFTn5eX+0N0umzEskWcVjPj0TS9bQ7SxTITsSypfJnXT0+RzJXIFsscHZvmz+7bxEAwSb5Q\nO7e8o9VKvlQlkiwwEkmTK5RRVQVFgWgyRzhZoMVmpqPVhkGFY2PTdHvseFuaGApnQIOJ6dr/1qzx\n2gkmcsTTRdz2Jm7q89FsNtDX4aTba+e1U2ES2SIGRaXZbOSuLR1kimUSmSJuRzNTyRzvv6mXSLpI\nq9WM09rE746Mo+lQLFdI5UqUq1VOTU5zw7o2buzzkS2U6XTVjuw0GlRu7PNyKlhbgt/S7Z73fwoW\nC4ZOWxP7TobYtcbDgYEw9+0I1F9zvn9zssx++UnYvApJ2BRCiOVbKlwYVIWqprHW18KxsQRbulzs\n6PUQTuWZiGfp9thJ50tsCvy+Gjp7LZe9iUPDMdpamnl7MEI4VcBkVNnR7WI4mqFY0YinC+iazs41\nbXhaLERTBQanUuzq9dDtdTA8labbY2MglCaZK+F21Cp6rTYzb56J4ndZSGRLKIrCm2cidLhqAbJS\nrdbHHyUyJdKFWghVFIilC0RSRfLlKgOhJOWqTq5QZiyepVCqUqrqBDx2rE1Gms1GrE0mToxP83/f\nv4X2Visv9U/R5bZzJpyeOXqy1o2+tbuV7T0e3hgMYzSoxLNF2lutHBqOEnDZCMaz+JwWAm4r+UqV\nHm8LQ1NpLGYD07kS8UyJTKGEx9HMYChFe6uF2zZ2cGQkxkAo/f+z96Yxdt3nmefvrHffl6pbK4tV\nZLG4idRCapclR44iJ94Su3vanUl3D9DpNHoGE2QAD4JGEiBBYMTADNCZIIPMfBhMI0E7cJwMbCuW\nZUWyRG20RFHcqshibbfq3rr7du527tnmw60qixIp27IlWtH5AQKhU/csvOcP1Mv3/T/PQ9Cr0ujo\nGKaDKMD8eIz1UouNchuPKqH1DO6bHyETC9zwHm9VGA67n1FevVrk0WPj+FX5J14vt+qEu7x/3GLz\nI4hbbLq4uLj8fNjteoZ88jCTu9VDEkXunE29y0vz7UiiwHQ6xJVsDa1v4FMkgj6FrVqX/sAiE/Pj\nUUQqmo5Xlnjucp7NShtJFNANk97AwHYgX++SCHm5/9Ao+VqH+fEoWs9gMhEkW2kT9SkMLJtW18Aj\ni1zdboAAHmlYgHV0ky+cnsG0LLZrPTyqjM8jo0oikijgYFNs9ZlLh9D6Bomgh1bX4MBoBMt2aPWG\ngqCeYXFls05HN7BtB3knkcewLDySxGpJI1vR+OSxcSIBD44N6YiPfK2LZdv4vTKJkI923yIW8JAM\neXGc4ffUN0yKjQ6fvnMarWcS9Kkkgl4ubNSQRHEvqhMExhMBFFkiE/NT1XROzCSH3WVRIBP70e+8\nm/mfvrMwVCSRA5nIT7094+NmuP5h4AqEXFxcXFw+tmTLGoV6l61qh2ylhSgIHJ6M3dRLE34Uf/g3\nL2lkISAAACAASURBVF7juUs5is0ed+1P8plTM3T6BookcmQqhiA4+FSFXzk5haJI7EuGSIV91LQe\n59YqLOeb3DOXxnEgElAZGBYODqZlIwjwyJEx0mEvmXiATNRPLKBSbPaZToXwKhJt3aTY7NEfmKyW\nWqwUNCRRIOARWdyqsVFus1lp0+waYNuYNoxEfdg2IECto1Ns9sjE/IxEfGTLLVrdAT5Z4mAmigBU\nW326uoPWH5AMemh2BlzI1rmyWefqdpPl7QYXNqoczESxLYdL2SqyJPDY8QnOr1X5/lubXMzWAJgZ\nCTORDFHR+tx3cIRis8u+dIjpVJDJZIgLG1UeODSK3yNzaDwKDIU8hmmjyiKHJmI3fP+78ZOZWIC/\nf20Vx/n5rYlbiY5cPlzczuZtxu1suri4uPx8qGo6Yb/KsakEhuXgVSRGov73/PyBTIStaofpVIiQ\nTyFbblNq9fjsqRmanQH3z49ybrVMsd5jraTR7g9YLbbwKTK6afJLxybQdIuxeABBAMdxyNfajMUC\nbJTb7M9E6Okmjxwd5/yOmflKUWMmFaai9Ql6FDq6yRN3TnPX/iQvXNlmMjk0M9+qdhmJ+ujqFndM\nJ4kHVWwHHCDkUwj7Pfg9Mo3OgGhQQTccmr0BiijRNcy9RKOwX0GRRVRZxKfKbFbbPHBojOVCk//w\ny0dQJZEXr2wzkwqyXtHI17r85y/ejeM4XFivkqt1+OypGbZqHZYLTQ5kojt+mSP0DIv75odWRbGg\nl42yxoOHM+RrHQ6MRcmWNaqajmFa1DrDbQzvtC3ajZ8sNIbfe8Svuvsrf4F5P3XLT77xwcXFxcXF\n5WdkpdDEdoadLoHh6Nm0bDKxwA2FyE9yriiKWLbN7OhwvLpR1tANk8vZGpPJAHOZ+E3P3f38rm1O\nvt4hHvJwYb1GOuLl0aPDfYGZmI/VYgtJlGgPemSiPq5tN+n1dXKCgGVZ/NcXlvHIIsVGh/nxGFvV\nNtGgj4qmc3Q6zoX1CqIgkq91qLV1NittDmai1Lt9bNthLBGgoumsl1o0O0PRTSzo5dxKmcOTMV64\nkufgeIxcrUPEr3Lf/CjZcpv58QgXNmrYtoPl2GyVdPaNhgl7Va7vxFA6tkMs6EEQBEI+lalEiH98\nM8vMaIiLmxUysQBPvbHBWrHFaCzAwmSMM0sFREHgu+eylHY6vRWtTySgslpo8Xu/dgdPv7XFp05M\nMj8W5eWlApVmj3ytQyLsZSIR2Osmwo+sqrq6yUZZu+H97n7/ggCSKDKR8GOYNmulFgfHoh/UEnS5\nDbidzduM29l0cXH5OPF2EcjFbI3pVIgjk3FKrR6t7uA9u1nvPPfUXPoGMYluWGh9E9O22ai08asy\nVa1PVeuTrXZuMG8fGp4PTd4/eXyCb7++wcOHM4QDKpc36zgOWLbDc5dzFBpd1kstZtJhshWNiN9D\nwKvQ6A6YTAY5Pp1kcatOJKDiU2VEAXyKzLde3yAd9ZMKe8nt5J4/eecU+VoHURRIBr3MjkZY2mpQ\nafYI+mQOT8WpaX28iszlzRrpsI/VYpNk2ItHlphOh4cF9WadlUKTRkdHQMDGQRQE6h0dfWCCIAw9\nNHeSgEzT4vJWg7tnU1g2PH7HJMv5Bkv5Jp+5Zx/bjQ627TCZCDKZClBq9ABQFAmfKvGDy9ucmEmi\nyBKCAFG/wuXNBpZjM5EIkor48HsUbIcb3uN7CXR291POj8fIxPzEgl53f+VHgPdTtwiO8/PcHeHy\n01IqlQBIp9O3+UlcXFxc3pubdQd/WpZydaaSIdZKLRzHwbQcLMdBFkUOT956b91KYbi3LxX27XTC\nBPaPRPa6YIokspSrY5g2oigwMG28isR0KsSZpW2mksOYyVeXi3zqjknWS9oNHbfvX9hibjTMhfUq\ngjAUt4R8Mj+4vM2jx8Z55q1N9IHNWDzIwkSEaltnYJjkal2CXoVSs8ehiSjN9oCwX0XrGQR9CuPx\nAOXtAo2uiU9yaAg+FFnm8RMTPH8xT8ivcL3Y4vhUnHbPoN03kUQot/qYjo1Hlnjk8Bhnr5fwqTIh\n77Bw86kKqZCH7sAiV+/Q7hpMJgPkam1qbR2/V0V0HCwHwl4FQYSJeJCL2RrpqBfdsHloIYMsC8yk\nIwxMm3Kzy750mI2yxmjUT7aiMTc69LMsNnokwl7eXK3w6/fv57VrRaZTIZbzDa5s1gkHPKRCXjyK\nwOxoFEEYZswDXN9uMpeJkC1rP/H6+XmsNZcPhvdTt7hvz8XFxcXlJ+JW6T0/DbOjETbKGgfHontm\n3qosvWehuXvvTxwdx2E4Pp8fj7FR1pBFgTNXtvnrF66xUmhw9nqJ0aif7XqHsXiAM4vb2LZNu2/y\n0tI2jx4dJ1vWOLtc4ttvbPDsxS2m0yHCPoXRWICgT6XVN/CpEgPTQZEkfIqMLIkcGo/hUQTeWq9S\nafWHgh2GheGhsQgRv0qx0WOl1CLkV8jXOhTrHRaLXcLJOE289Fot/uOTRzk8EccRoNHRyUR8nFnc\n5sJGhVeXi2j9AQsTMRqazkjEz8tXi4R9KiGvSqNrkA57ubhRZbvRYaXQZDAw6QwMeoaJ36swkQji\nEQWiQS+piBdHhOlkmKV8g0hAodUd0NNNXr9e5uk3t/j6meu8uVpmIhFg/2gYcMjtjP1102a93EI3\nLYqNHqmIh5cXC9j2MPlHEARCfpV02MNUKogkSixMxPbWyNsFOj/N+vl5rDWXXxzcMfr7QNd1vvKV\nr/Dcc8/xp3/6p0QiERKJBL/zO7/D66+/zj/8wz/wxBNPIAgC3/rWt/ja177Gd77zHUzTZGFh4YZr\nuWN0FxeXjwo/D8/Ct/tkSqLASHSooH57Dvbbjb3XdxJzVgpNRqN+Orqxt7czFR56V4b9KomQl4BX\nJRX2sbzdRJbg2Qs5tutdDk/EcIThfSfiAao7iTl+j4zfI9PTTXTT5lquydVcg+lEgPVKZ6gu96lU\n2jqPHh1jYSKGTx16WqqiSLWtMzcaIRHycGAswrd+uEEy7CXkU6i0etw1m6LQ6DHlcxBVhUG7hScc\nodUzyNU6mJaNV5FpdnRKWp/DEzHCXpX1ooaNQzzoJeRT8XlkxuJD/8sH5kc4s1Tk5L4EHlWm0dHZ\nlw4T9CmsFTQUUeDgaIyuYbIvFeLoVBwJke1mFwEQBIHRqJ/RmJ9qu8+x6QQ44FUlRqMBerrJPQdG\nePatLXwemZeX8uCI5GptfvXuabKVDooElg3PXtjinrk0tmMzlQzh98iE/QpBr3rTNVJu9Sg2elzM\nVgn5VGLBWyf6uP6Yv7i4Y/QPiWeeeYavfvWrPPvss1y7do17772Xz3zmM3z2s5/l85//PF/5ylc4\nePAgX/ziFzl58iRXr15FEAQOHz7M2bNniUR+lN/qjtFdXFw+KhiWvTcS/aDGmsvbTUQBurpJrjZU\niS9MxGh2B7y8VOCX7pi44d5LuTqrxRa56lDksy8V5vi+BC9cGY7Oa1qfbEWjq1ucOpDeK/IGls1I\nxE9N67FvZx/k37+2xp2zSVpdg0vZGg8fHsOyHa5tN0mGhp6W6+UWEb9KIuwlHfbvdN0cio0eflXm\n1MER3lgpsV3v8uRdU1g2CI7FyloewR8iEvCyUdLYnwkzFgvQHQzYKHWotvrUOzq9gTFUmasyc5kI\nGzt7RGMBD6Vmj2jAg9bTmUgGuZSt0dVNWp0Bogg1rU8s4CXoV4gHPDR7Br9+eoa/e20NryrR1U3C\nXgXHgb5hMpUOo3UGIDhMp0L0BhYPLmQQBPjuuU3mx6OslZpIggCCgACkoz6mkyGKzR6xgIdsRUOW\nROZGI3uWRrdaI4Zl8/Uzy9w1m0Y3LSRR5NhUnJvxYaw1l/eHO0b/kHj88cf55je/CUAymUSSJL73\nve9x3333AXD//ffz1FNP8eqrrzI3N4csy0iSxOHDh3nhhRdu56O7uLi4vG8+DM/CXYWyKArcNz86\njGfUTbaq7XcVmgC5aoezyyWqmo65s19TkUQuZqs8d2mL7721hW073Lk/iSqLGKZFod6lUOvy7dfX\n2a73sGyHmZEIYb+Hew+M0u4bzI2EqbT6nFstE/YpvHatyEa5xZ0zSbYbXbKlNtXWsJCtaDpeVaas\n9RBxKDa6zI6EKdZ6VFo99o1EkcNR1kptqu1hWk99Z8/nS4sldGNoHF/WevhUhXTERzzk4cuPHGQ8\nHsSybE7OJJkdjXB9u0nY52Gj3CbkVQl6Fdr9AeGAiigKdA1zmDyk9ZkdCfH0+S38Hpm+bnFiOgGC\ngGnbHBiP0R+YFJtd9o+Eh+brcf/eCNywbLJljXKrT6Wlc2JfgpOzaaZTYYrNHskdAZBHkTg4FsWr\nyvzgcn5ntH7r9SMgYFo26yUN07JvuQ5cf8x/Xrhv8X2y25386le/yp/8yZ9QrVYJhUIABINBisXi\nDccAQqEQxWLxtjyvi4uLy+1m18R7KVfHeEehsfsz07LJVjQmEkG2qm0ePjK2t8czW9befb4g8MCh\nDA8fzrBaapOvtbmaqxP1q4iiyOxIiM7AIlfrsJRrUGj26Bsmm9U2U8kgA9Ok0dE5s7TNI0fHePVa\nkVMHRrh3fhQEGI35WStqFJtdam2dH1zO41dlHjycod0f7tmcSYXwqyIHRiP8/dl19o8Mu3zpmI87\n96cIehVGIj46A5Pr2w1eXMwjAqvFNp84MkKx1WWl0GI8FsS0HRxn2Nk7t1JG6w5odgZ84+UVnru4\nRSrs4Wq+gW07XMs3+OTRDAGvimU5OIJAOuKjN7CYSATZrHaYTAXBYdgJ3azhUyQePJxhNh1ClSWC\nPpnr2y0abR1JFDm/VmF5u8Gjx8ZAgCNTCeYnwoiiiF+VODoV55PHJ/AoEn6PzPxYlEKjh2XbJEJe\nACzL5rlLOZZyda7mGze8s0TYi2k7ZKJ+ZHc0/rHBLTZ/Bv7yL/+SYDDIb//2b5NOp2m32wBomsbo\n6OgNx3aPZzKZ2/W4Li4uLreVm4k+VgpNnruY4+WrRXTDZDIZYioVJl/rcHAsekP6z83ON0wLv0fi\n229skAh6MW2bl64WSYV9PHFikq16h3/54Bz3zY+yuNXgM3fvo9HRGYsFiAQ8lFs633x1le16B2yH\n/aMRgl6FQqPLwLC4vFkjX9OQRWknMnKonK+2+rT7A0YiXl5ZLlLvDFjM1RmPB7CBV64WWM43eWlp\nm6fOZXn+Uh7DtMCBBxdGWK+0MS2L64UOfkWmPTBRFZFMzIfj2EQCKt98dZV236BvWrT6AyYSQZpd\nnUKjS7s34IFDI7y4VCIV9lLX+oS8CsVGn7lMmCubNTp9gzeul4iFvOTrbbSuyWjcz6WNGj+8Xqbc\n7PLYsQnKWo/z61VeuVrgpasFNsoahyfjTKdCPH58goDXQ6XV20v2UaTh+PvYdAJRFBmP+0mFfZxb\nLTMWD7BZ7TKdCmGYNq9eLbIvHdp7ZxOJ4T4/ryrtjd1d/vnjFpvvA8dx+OM//mNUVeUP/uAP+N73\nvsfhw4d5+eWXAXjllVd48sknuf/++1ldXcUwDEzT5MqVKzzyyCO3+eldXFxcbg+7I/K1Uou5zHA6\nZO903RIhD9fyTb7xygqzo+EbRqi7Xc+VQpNmd3DD+Q8fGaPeGTAzEuKeAykm4iGiAZV40MN339zk\nsaPj4LAzhh8nW2lzaDxOMuzFcYbxiSf3J0iFvDxzYQvTtMhXOwgIbFRaCM6wW+hXBbyyjGPD7332\nDizbZiTqJxnyE/Mp+FSZ+bEYQa+MTxGpdwaUWz0M02FgmuwfCSGKAuDw6rUyAY/MYq7BUr620810\nUCWRbt9CFCUGhk3Aq6D1B8R34iUvZquUm31kaXidF67kUUSB+w+N8Ojxce49kCYeVGn3TLyqzGQi\nRMCnUqh3mB2JMBLz7qQrWTR7Ol5F4qWlAkGPwqfvnuaRw2M02jqZWICXFwtMp0K8crXARDzAr9w5\nzf6R8LuU4ZY9NOR/dbnIb9w/S77WYSLhZ2BYbNe7RIMe/vHcBss7+y/nx2N4FYnDk3F3RP4xwhUI\nvQ/+4i/+gt///d9nbGwMgHq9zlNPPcWf/dmfMTU1RafT4c///M8RRZHvfOc7fP3rX0cURT7zmc/w\nhS984YZruQIhFxeXjzo/qSfi1VydjUqbsZif+fHYnjfmarHFVrWDgMNn7pmh1tb3EmhWCk3Wy+29\n7tnZ5dINezd3730tXydX7YIAAg7paIDHdz63KzQB+D+fvoxpWVQ0nalkEK1vsFVu4/PIpCNeDNMZ\nFpFhL5uVNtVWj9WShiSCIkvcP59hu9FhtdDEsGxUSeTBhQyqIvP0m1lCPgWfR6bY7HFgJErXGFCo\n9zBMm/7ApDcwSUd8pCI+UhE/59cqGIZNyCujWxbJkI9owMPCZIxio0ujrXNxs8bds+kdZX6f6XSQ\n9VKbA5kI4/EgVa2HaUPEr7Kcb5AIe9kqa3z+9H6evZTjjqk4PdPGth3iIQ/FepfR2DAS07RsVgpN\nTh1I8903N3nyziliQQ+qPLQw6uomL1zJ8/DhsRs8TXe5mZDHsGyevbDFSNRPodFhYNjIksCnTkzd\nNJ/e9dP8aPF+6ha32LzNuMWmi4vLR53l7eYNBukLtxiP3uxzhmXz/QtbWJbN3XNp3lgt86k7JveK\njucu5sjVO/hViWq7z5cfnsev/ihp+bmLOSaSQZrdAefXSiiyzPHpBJsVDVWRmR0J31DI/Pk/XuTw\nRBxFEnhpcZtj++K8tFgkFfaiKhKleo9qp0+7ZyAKEPDIjMT8tHoGumFxdCrBlc06CJAMetistnEc\n8HtkOrrBeCxAe2BiDCwOTEQpVLsUGh0kSaTe1gl6ZaqazmgsgD4w6Q4swj6FnmEhiXBqboTN6tBa\n5uhklLMrZXp9g4eOjPPG9TLxsIdSo0fAqxDyyciixIMLo1zaqLBcaHFoLEq1raNIEhsVjc+fmqKj\nOzxydIwfXMxh2g6SCFVtwPF9cRRJYiIZ4JuvrHFsX5yAKnNoIsZKoblnvj8zEmajpP1UyvC3F5yq\nLDKRCJKvdd61Nn7StePyi4OrRndxcXFx+dDZKGtc3Kjy/OUc0+nQLT93szG6IonMpEM8dHiMN1bK\ne6brz13K8cxbm6yXNebHogiCSDLo48yV7T2xyUqhyVatQ0c3yFU7iIKEYVoMTIuRaADHthEFMMxh\n4WNYNgPDptrq8e03Nmh2DS5vNHBweGu9SrbcZrOqEfaqTKYCeDwyfq/C7GgEVZYYjfqJ+FUs28Yj\ni+SqXZrdAbbj4FUlbAsqms6J6SQO4FhQanUxbIYFqarQ6hpEfCqtzjCS0quIeBQRfWDikUVeWy7i\n7FxvcavBkyen+DePLbC0VePwZIx0xI9lOwjAZrmNPjB4abFAoamTiQa4tFWnUO9S1npkIj7Cfh+/\ncucUlWaPA2NR2n2DetckE/eRLbfZqmjUNJ2HDo9y1/4Ux6YTO6pxOLO0jeM4e8rwmwq0boEiiXzy\n+ATFRndP7LX7zn/cmnD554dr6n6bcU3dXVxcPurohoUoCgRUmcWtYSESCbzbsHs3C/ud2deRgIeN\nssY9B9J4FYm31qrYQDLspdMb7tE8MBpBNy1+6Y5JQl6VtWILQRBIhb2sFIZF0P7RELX2gKWtOn6P\niCCIVLQ+7b6BYVqcXS4jS3B5s44siOwbDXN2uYQqiYR9KqVWD9txqHX6NDoDon4VSRRZL2lEAyqx\noJdWb0BvYOLYDuuV9tBzstFFFARavQEDw2S73uWeA2lqbR3LdlAVkXKrT609YDwkY5gG+yIevD4v\n6XiAxa0GigxVzcByHB5aGEWWhuf4VInnL+dpdYdCIXAI+xRaPYNfu2eGa9stJlMhDmQihH0q2Uob\nnyqxLx1mIuEnX+8S8MhYtsPCRAxRECjUOzx6dIJzq2X6ps143L+XGb+baV7vDDg1lx4atO/EhL61\nViXsV+kNTGptnZHIu3Ps327KHw962T8yjL+8Vd75rdaEyy8u76ducd+si4uLi8vPhCDAdCrEdqPL\no8fG3xUx+HaBz81GsYokosrDom4pV0c3LRJBD92+iSRJ3DM3gt8jM50KY5j2XhfMsm0md0a9CxNR\ndMNiNOZnYSLKzEiUBxcyLOUa3Dc/yrXtJtV2n2TIx1QihCA6tLoDfKpEOODBcoYincnk0H7Ir0p4\nZIl4yMvhiRib5Ta19nC8vi8dwkFgIhGg0dERRWj2BnhVGb9H4d8/vsC1XANZFjBMm6lECFUSmRsJ\n0ewbTGUStA2T4KBGrVIn5JMREAl4JCTgqTeziILA3GiE1aJGT7c4NhXHI0tEfCpblQ6W6fCNV1aw\nHJvD41FMy6Tc6g2LTr/K/pEwa6UOX3pgjqlkiI2yxtPns3zvrU06fZPvX9hkJOrn7tkUqfAwLnO3\ns7hSaLJe0ji/XtkT9gDkax36A/M9PTLf6Rjwznf7zo6o66f58cDtbN5m3M6mi4vLR53d7lTIpxLx\nvzuqsKrpiMKwA/rMW1uoskRV698QR7lW0gj7FAzTZnGrgW5YZCsaR6biHJ2K09VNHIfh/k7HIVvW\nqGp9zq1U6OomUb/K4lYdy3bQTRvDstmstImFPGRLLS5ka/g9Mvlqh0pbp9bu0+kN6BkWjY6OYVr4\nVBnHgcOTcaqaTt8wabR1YkEPW5XOcITtONS0AbOjYQzLodkdMBL1E/AoNDsDHlzI8P/9cJ2gV2Ui\nEaDW1inUO8RCHjyyREi26RkORwM6JTlO2O8hV21jOeBVh+IiVZIYifpJR4YZ7z6PQiLsY2mrzqfu\nmKTVMwj7VSJ+hcHA5sJmjclEkHjQy8CwUGSJjbLGobEI06kwr1wrMBYL8MyFLZ44McXlzRpX800G\npkU67ONqvsEnj0/gVaS993ViJkmp2UMSBTKx4e+nfL2D36PgV2U8yvAZ3xkvWtX674qZrGo6BzKR\n4fGdLuk7z3PjKD86vJ+6xS02bzNuseni4vJRZzfvPBa8+Ui03OrtjdqTIS+yJGLbDhc2qjQ6w8Km\noxuslzTiIS/3zKX5wZU8jx2bYLPcZq2o0ewa3DmbQpFE1ooac6MRXrlWRDcsgj6Z1YJGKuKj1Tco\nt7psVjrkqm0kEX54vUIi5MXvkdiqdjh9ME2ja9DWDUajfnLVDsemE7R6JqZp41UVPIpIpanT6g3I\n1boo8lCNXm8PMCyLVnf4Z7tnMBcWGRgGv3YgxIVSn/vnR9msahwaj1Jr9xElAVkWifq9WKLEoNfH\nlGS6Flj9PrLXg1eR6Ogm0YCKIECtreNTJLqGiUcSWMrX+dIDM2QrHbyKiCCKVFp9FEXkrtnhyD4R\n9PCF+2Ypt/qcOjDCiZkULy8NLYyOTMVZ2W4RD3nZKLe592CaX7lzmhcXCxzfl2QiPvwdtFJoslZq\n0embiMIwIWjXxsmrDIVZiiyyMBm/aSE5l4m8aw3cLOf8ZgWoyy8upVyD175/jW/9v2c5et8E4I7R\nXVxcXFw+JN6eCgTsjUTffnwqFeLNtQoDw+KfLuVp9w2+99YmmbifnmFxfr2KgIAgwEQiSKHRJREa\nZohfzNYwLJNYUOVStsZmtc3AtFgraRimjUeRiAW8tHWTentAuzfApyi0+zo9w2Jxc2jrY9k2l7I1\n7j2YRhCGynDbcqi1dVRF5PTBEeZGI0QDKiMRL1rPIBrwcGg8SirswXIEqm0drTfg2HSC9sDkoYUM\n/8MvLVDvW3z6vkM8uzngVEpA6xvIkkiu1iHsV0kGfYyG/VzdrnMt30L0eunLPpp1jahXQjIHRPwe\nplIham2deMiLV5G4VmhSa/bYbvYZi/v5zutbnJhJ8pl7ZoaiItMi4BnaHQ1Mi4FlY5g2kghTyeBe\nxKewkzVf6/R5/lKORrdPwCvz8tI2d8+lODQe3XuftgOfODqOA4iiiCiKe2NxSRSotvqUmj2ev5Tn\nUraKblg3CHxuNhafHY3spUDtHneFQb+4OI5DdrnMU3/9On/2P/0dv3n6f+M/PflXnPnHK0zsT76v\na7rWR7cZ1/rIxcXlo8ytrGveeXyXdt9gcbOObtmIQCSg0ujo/KuHD97gifnshS2uZOsMTIuATyET\nDzCfiTCbifDfXlzm2HSCtVKL5e0WIiAKw32Tlu3gVSRaPYN2b8A9syO8la0ymQhgWDZRv4pHlUmG\nvPzTpS2SIS/rZY1owINtDUU0Hd1kKVenpvWJBb30DZO5TJTlfINHj4yxUdEoNfv88olJ3lgt8++P\nBfjmqkHM6WAFY4T8Hs6vVplKBqh1dLq6RSSgsrzdoqsPkEQR23EY9wtMT46yUWww4bPI6QrblSZx\nn0zXsDi8L02xqXNkOsb5tRpTiSD3HEhTbfeZG43w0lKBFy7nCXhltJ7BeDxAPOgBQNoRPX3xgVn8\nqsz17Sbr5RZ1TUcUBV5eKvLfPTTH3XPpGwrDpVx9z/Lo4Fh0zwLpzNI2kzuq8olEkOlUiKv5BrIo\nIEviT2WLBDf353S5PVimxfVLBS6d3eDS2Q0un83iDagcPTXNsdPTHD01xcRsEmEn9N712fwI4hab\nLi4uv2jczGj7Vubb7yxObnV8t2h5/nKOTNTPVq1NvtpFkkRO7EtQ0YYKcMOysGyIBRXOLpf55ROT\nbFU7dHWD2ZEIpu3QGxhkYgFEQeDiRpX1soY+sHBwmB2NDvPR800OjUe5slkj4JE4fSBDNKgiCAI+\nVeLayhavb2p4ZYFQwIcjiNx3KMMPl4vcNz/C6ytlDNum1OjhUyT8XoWBYdMbDAh6VcJelZnREHOj\nEa5vN5iUOpjhFLrlcG61Qr7apq2b4EA66qNY7yJJArblgCBwaCLGqKxjOzbNZptDEzFKTgC73eS1\nbQNJdAiINolEhN7AoqdbjMb9dPsm+9IhVnZM5bNljdGIH1WRMCyHiYSfxa0GX7h3huPTCf7pYp5H\nRh1sy+bMWhPDG0Tr24wnA7R7Jr/16Px7mrTv/j8Mu9bn1ypkKxrj8SCyJOwlAbnm7B8d+r0Bp1hT\naQAAIABJREFUV9/McfG1YXF59c0c6YkIR09ND/87PUXqPbrN76dukX/8R1xcXFxcPk7sKoq7usn1\n7SYLE7GbHgMQBIEzi9uMxW/cvzU7GuH6dpODY1GyZQ3HgW+8ssKx6QTn1yo8eecU/zV3lX/72ALr\nZQ0HSEV83HdwhO++uYEkSuDAC1e26eomsYCHt/pVAh6JaMDLpWyNU3MpNittLNum1Td49Mgor69W\n+PV7ZzHtNQzLIh70ovUN3tqsMpcOY9g26YiPy4UOQb8PAZtqq0s8EuT5S0Nz+e++uUkkoBLyKhw6\nHKOnG6wUW6QiHq5ku5w+mGGj1EI3bV5bLrIwEafQkdnONig0uuiGRcjvwbYdDNumqxuIgsDAtJlK\nBim3+vQHJlYoRLuQY9P20833WK/WiXlA1yUmIwIjyQTLJY25TJSa1kcRBf7jE0f4mxeX8akyv3ps\nnP/92xcwbBtDtxmYNobpJR7ycS3f5NWrRX7vcyeora4gpic4qoT5v7+/yL967AhVTefIZOiGd7lS\naJKttDEtG8t2mB+P7o3Fl3J1urqJIMBkMoQsDgvm3aLyVuvD5fbTqne58vrmsHP52gari0X2L4xw\n9PQ0n/t393L47knCMf8H+gxuZ/M243Y2XVxcfpFYKTRZLWqkIl4EBA5P/ihW8u3jVEEYFpTrJW2v\nyHh5qcB0OvSu7tbuSP3iRhVZEhmLB3h5qUAm5h/uDRQEJFFgtdjCtCyWt1s8ciTDhY0qY7EAW7UO\nLy1uM5EIUu/oTCVDfOrEJFc2q6yWNHq6SdArU2j2+XePHaLVGfD1l1eQBGh0DaZTAUTBQetbHJ2M\ns5RrYPY69JEoNnqMRIZ7R9NRP4V6j5BPxiOLFFt97j2Q5lK2hiyJGLaNPrDYnw6zUmoxtmPyPhYP\nYjoWNU0n7PdwZbNOd2DQ6ej0TIcDYxG03gCvLGPYFv2BxVQqSCLk58GkwbObFkekCldIcy3fRBp0\nUUNhLEdAEGzCPhWvojCRDNLTh8KdvmlSaw9odHpkokEqrT6xgMpkMsRKsYUqi/zWY/NoXQOhvIkR\nSiI2ijS8CV64WuHhIxmCXnXv/e6+J4DxeICr+QZeRdorGn/c2PtWHW6XD5/ydpNLr2X3xuKlrSbz\nJ8f3xuLzJ8fx+tT3fX23s+ni4uLi8jMxFIiMcSlbG+aB7xQNu53KyUQQVRbp6ibPXthiIhHcE3qM\nJwLv6m7tejZ2dIPNSpuHD4/tCVeAG3LLDdPizbUq/+GJI5y5sk2zM+COfUleuVbk0FiM0Zif5y7l\nCfsVtnaEQmMxP4ogcTlX418/dIBWZ8ClrRpRv8pqqYlfkWj3TSxrGNnY0w3CfoUNTaSv91FlkUbP\nwLYt1ksaIZ9CJKDgOAL/6YmjvLi4zZcfOsDfv7ZOJOBBHxhc3W4SC6qM7YyiYyEPlWaPoEelpvXx\nyCJhv4+NgUnaL7NZGSrT18ttZFHgxEyKhYkIb6xU2L8wz99deg11OsqFSzWSHpu2z892o0c0OFSp\ni6JApdOj1Rvw+dMz+FWJV5dLDAY9/scnj/ODS9scmYwxGgvw7IUtjk7F+dSJSV67Vhz6hkZH2FjZ\n4KHTRzCrfZ446UOWJQ6N31gUWrZNf2CxmKsji+INwp1sWUPeGZffbEz+9k62W2h+eDiOw9ZKhUtn\ns3tj8X5nwJFTUxw9Nc2nvnSS2SOjyDu2VrcLt7N5m3E7my4uLr9I/LgO1VKujmHaiKLARCK4V4TM\nZSKsFJpYls1mtctEws/8eIz1ksa+dIi/fuEalj0U8nzhvv1EfCrPX85h2cMi8+EjY/hVee/+f/fq\nCkcn4/xwpcTlbB3TtujqFtGAyoMLYwAIwLXtJtu1DgICR6ejZCsdbGs4vs6WNbyKxL6RCA8tjPDG\napX7Do7w1LkNGpqOIECl3UeVJPqGSWdgMRb10TNMTs2O4FVlbGxGo0GWtuoMTJtSq4tHkchE/PRN\nG0mAWMhLPKjQ6lpE/Qrn1ioEvTJaz0SWQDdtys0+9x5M88ZKmdGYH9Ny+J9/7Tg1TSce8vDn37nI\niZjNN5fajMcCmI6DKkscGovyxlqZ++dH2D8SIeJXkSURUYBWd8D59Sr/4sE5NkoauWoH3bSG3dqo\nj6lkkGPTCbq6yUqh+WOFPIZls7RVJ1/rMJUKIQjDbRKiILBe0vjE0TEM03YzzG8jlmmxeqXIpbMb\nO8VlFo9XHnYt7x3uuZyc+5GY54PAFQh9BHGLTRcXlw+bt4s5douJ3dE38J7jUsMa5ozfNz/KVrW9\nV5CuFJoMTJuz10t88f5ZcIaZ6YIAU8kQf/XMFX75xCSt3oByq8+v3jXNsxdz7EsFqbV1LmVr/OtH\nDu7df6Os8fDhMRa36vz1C9cIehXa/QG66fBvHp3HdkCVRZ6/lEPrDejpNkG/gm1ZLBdbBFWZaltn\nIh4gHFAxLRsBgWKzx0Q8QEXrkQj7uLBaJuRXqLV1vKpE0OfBsR3mx6KEfCqt/gCPLDKRCPIPZ9cZ\ni/to9038qoxXkdk/GmZg2lzN1Ql5FRAE0mEfPcMgV+vi2A4+j0y52ccwLZIRHwPT4vBEnIrWYzoV\nJhX2MJ4Isl3r8PLVIrOjIV68UuA37p3hhcUCx6Zj9PoWoiSSjngZjwcoNHoAJMM+Kq2h+fpWtYPj\nwPF9CRzHoar1efjw2E892n67k8CZxW0+dWKS8+sVnJ3v/F3XKqyBbYNtQWYGJOVnXKEuu+g9g6vn\nc3sj8cVzW6Qy4RvEPOm3WVd9GLhj9PfBxYsXWVlZYTAYMDU1xd13340sf+y/FhcXl3+G7BaZb+9S\n7RYTbx99v1fXSpFEPnl84l0j02xlaIkTC3r4/lubqLKEIAg4jsNryyUGA4tY0MPiVp2ZdAgYdjRr\nbZ0Xr2wT9iv8l+9c5JPHxjgylcCy7aG1jiQQ8Cp8asdmyLEd8rUOayUNBIGr+QaZuB+PR0QV4M1c\nk4A6tD5KR30E/CqVlk7ErzKVDvLYsXH+8c0sPlVBEkUmRkKUGj08qkLII1Pv6KiigN+jsFHROD0/\nSr3VZ7PaIR70UG7oxMNDO6T58SjHpxNsVduIAuRqHTLRAC8u5XFsh8lEkHKnz8C2gWHRKQgwOxLB\nq4hMJsMkQx56usVGqcVKQSMZ9rG01WAqGeR7F3IcGoswMxLh1atFnjg5xZmlbV69VqKjmzwwP0Jv\nYLJW0nj8+ATnVivMjoQZmBaVVp/Z0fBNxVu76+C7b2aJBDxYls2dsyn8qoxl25jWjzwwx+KBoTAI\nAUnk5kWrbcPYLOg92F6Fifmf67r9ONFu9rj8+iaXdkbiK5cL7JtPc/T0NL/635/if/0/fuMDF/N8\nEHxsO5tXr17ly1/+Ml6vl7GxMWRZplQqsbGxwde+9jU+97nPfSjP4XY2XVxcPix2O1Zv71I5Djs5\n2u9P2LFbuLy8VOBzp2dYyjW4nK3ym5+YZ2mrzna9y4MLGb7zxjpvrdeYToVQZJG79iexbJvXVyoA\ntHsDxhMBzq9VOTAWJRX2ImAzMxLlhcs5is0esaAHr9FBAmpdnU8/cIStWo+/eek6C+NxtiptBNGh\nN7DoD0xEBNIRP6oqMhrxc2WrTkp1UCSHiCqhiT7GkyG+++YGUb/MdmOAKsFcJoJhOWRiAfwekTOL\nJSYSAdZKGpGAzFgsQLtvonUNbGAyESBfqiELUNAMpkYjgECna9DqG8yNham0+pimTcCnoIgi9x5M\nc261SsCjYDs26Zifk9MJvv3GBh5Z4spWnRP7kiiqSLHRY386zOdPz/BXz1whE/UT8Ck7Nk0ZRAEa\n7QHbjQ71bp+ZVIR7D6Ypt/rsS4fp6AbFepf6jrWUaTn8iwfn+NuXVjh9IE3Ip/Dfzqzwv3z2juHI\nvdhCFgVEYbiHd6Os7W1zuClb1yA1ycriVezkJJYguvZHPyHVQotLZ7N7Y/FCts78iXGOnh52Lhfu\nnMDrf/9ing8Ct7P5E9Lv9/nDP/xDnn76aRKJxA0/03Wd3/3d3+XEiRPs27fv9jygi4uLywfAbmrL\n27tUAD+4lGM8Ebyl+ONWrBSarJfbjMf9HBqP8vylHPvSYU4dSGOYNpvVNumIn8WtOj6PjG6YqLJI\nWx+wWmoxNxJBkQTe2qihyiJa36TU7HLqQIpk2Ms3Xl5hpdRB6+oIgsBUMsRBVcSOZ/i/nrnC3/7T\nJdqCh/3pMI5jc3Qqxnq5jWEOP2+aNpIkkAz5qHV0EKDRG3DfsRkurJaY8PW4XhjaEa2W2gR9MgJw\nZavBqbkUiigwnggzM9Kl1R3gUUTK9T4iArV2H1kSmBuNcm27jl9wGM0kqPUrdLUOjuKlMzB5/I4J\nGt0BxXqP/ekIA9uioxu8uV4j4FMYGCZ37Etx//wIT53LcvfcCJVWj2ZPZ7vRZWBa3DOX4mquwTde\nXQUcvOrQFmoqGWI06qOi9TBsi6NTMbKVDrIkMjMS4a2NGg8cyrC4VcfGGVpLRXo8daXKM8+fo9SR\n8KkZnjqX5RPHxvbG5pPJoduA7Qy9NadTITZK77FPMzMD26vYyUkOTCRc+6Nb4DgOubXqDUrxdrPP\n0R0xzyd//Q7mjmZuu5jng+Bj2dnM5/M0m00WFhZu+vNms8nq6ionT578wJ/F7Wy6uLh8WNzKvuad\naT+qLO51tMbjgT2bo3cWocvbTSzbJhX28dLVbWbSkb3ow+vbTabTIV68nMfGQQBeuVpiLO7nh8sl\nptJBTuxLciAT5Wq+zrde32A6FcKyLBYmEqiyyItLeT51fJKqptPs6syORhg1a7xRlygXK+R1Ca+q\ncHAszBfvP8DXX1rhpcU8fdPCq0j4PTJRn0qu0SHkVVFlCb/VJ9dxkDEwRXX4dxLAMG1kaZjZHfIN\nxT0Hx6IkQ16W8020nk7fsFBlEcOwmEt6aHRNRI+Xjm4iWAMmUlHWt+vcNZuh0OkznQxh4wztlLwy\nhUaPeNBLWesxlQwylQjy3fNZ/vNv3MVSrsFDR8b45iurHByL8OyFHIVGl5MzCWxH4Ph0nEZnwJ37\nk/w/zy3x8JFxVFlElURmMxG+fuY6h8ajXN6s8cTJKd5arzIe9+Mw7FAOraU0zFqRshAiE5Q5HR3w\nnW2ZX79//17KEAwLzK5u8sKV/E+159O1P7oRy7JZW9wR87y6weUfZpEViaOnp/b2XE4dSCKKH63v\nyRUI/Yz0+33Onz/Pvffe+6Hd0y02XVxcbjfvLBJ2vTN3fTGnUyH+4bVVBEHAsGyOTyc4MhVnpdAk\nEwtwZnGbR4+Nv2vM+vzlHBezdSI+Bb9HYWmrRrbaJupXqWk6kgh+r7rXcZ0fj1Jq9BiYNkGfQq7S\n4chUnEKzSzLo5chUjEzUy8VLK9SEANeLGs3ugIhPYTwRYCTiY62s4ZEkWj2dRteg0ekzOxqh0urT\n003AwTEtepbDfQdHuZircXI6xfJ2g1Kjx2jMjyAI+D0y8aCHQqNDudWnq5soIliOQNwLouKl1dUZ\nDYjUdIE79ydY3iyTiocZT0ZIhr2srG/R7lv4RIFIMs5ivonfI2NZDg4OPlXh4FiUc6tlfvPhOcIB\nL53+gI1yG8txSIe8nFkq8G8fO8Qbq2UeOJRhq9pmZiTMRkm74R8N3YHJ189c58k7p3h9tcyjR8dv\niP8EuLRR5c0L1/iXnzwOpSwbSpqFqdS71oJlOWxW24zG/EjCjebt78XHPYJy0De4+lb+R2KeN7aI\np4PDyMedsfjIxIcr5vkgcIvNn5Hnn3+e3/qt32JjY+NDu6dbbLq4uNxuDMvmB5fyjCeGXUzTstk/\nEuH5y7m9AidX7TAzEiYe9HBmcZuDY1HmMpFbFhcrhSZPvZnFNG1O7EtwabOGIoos5hqcPjjC+bUK\nfcOkq1sYpsUDhzKcvV7iiTsneWWpSCYeANvh8ladkFfmj798mhcu53ljpYzWM2j1dLS+wcnpFGul\nFtV2j5DXgyBAwCfhkWTytTbtvsVIxEt3YNA3HDzyMJdcEAXa3QGCKDIS8SDbJkGPRLVj0rUELNNm\nMhWmb5p4FImtSgvLEQj7ZNptHUeU2BcWKQ1Egj4Vryyj9XQCXhWfIjGRDNKp13j83gWWNytcyxa4\n3nSYSYeYToa4slUn4lfJxPzopkW+3iUZ8jKdDLJa1Hj06BjFZo9M1Eex1Scd9iJJ7/bGfOd7/HHF\n3lK2zJRVYU1IcHAy+a7PGZbNcxdzPHps3LU5+jF0Wv0fJfOc3eD6pQJTB5J7Xcsjp6aIJgI//kIf\nMdxi8yOIW2y6uLj8IvD2UfpKsUWx3mW70aHS7DObCQMwEvGTq3eYToY4vi9BtqztxRtmYoG9eMPd\n6728VECVBc6tVZlJh/nyIwf42zPLFBo9shUNx3awERAFB1WSuX9hhKBX5YUree6YTvDacomxeABZ\nEECAQrPHY0cz5Otdzq1UqHd0ZEkk7FXoGyanD46yuFWn3u4T8Crohk1vYGFaJobpcHJ/kovZGh5F\npD8wiQe9WJaN5cDJtMrVpo1XEtH1DqWuQCbmJxZQqTR7IIpIAtRbXQ4mPPR6PQhEqXcMwgGFlYJG\n0Ctz6kAaAYF8rU2+3CQY8OEbtBmIKg9MBclZHroDh1TUxytLBR6/Y5ILGxVOHUhj2g7Zchvbtuno\nJhOJAKIo8sChUTKxAPla54bC75155Nmy9p755Lv2VO8l+LlVgpQL1EraUMyzoxTPrdU4eMfYsHN5\napqFuybwBTy3+zE/cN5P3SL90R/90R99QM/zkeBLX/oS09PTPP3003z605/GNE0efPDBD+3+nU4H\ngEDgn9+/flxcPu6sFJpUNZ1yq0ck4EESPzij5fe61/OXc6wUNa7lG4wlAjctHsqtHn6PwlqpxfzO\nvkvbgc+dnqHdN2nrBr2ByVQqxLHpBIokUtV0wn4VvyqzVtLYKGvDfZqLea5s1mn3B2xVO5ycSfKr\nd0///+y9eZAc532m+WRmZd331V1d1Xc3uhvoxkmC4CHwlkRZIZmWbdnyeOVDu7ZmNbMxHofDng1P\nxMrhcIRnHKPd0Vq79oQdCsvyjnVYsjyWLFECSZEgARLE1Q30fVRXdd33mZWVmftHEy2Ap6gDIIl6\n/qusqs4v6vsq+le/431Jl5q858AA8VwNiyRSqHcwiaBqMB31IAoiqVKTgMvKQryA1Sxht8r4nWYs\nsoTfaUUSBZ5cSGE3i1SaCjbZhEUWUbo6O6UmuUqLoNtKq63RVjUm+l1kKgoHhny01C4xvxOLSaLb\n1RiNeFFUjTsnwhTLNfJNDZ+sIUoSlYaKS4aaotPVDeaG/Gxma0wEzFhcHpq6RKFax2WzUG2qRANO\ndAy28w0UTUOWJO6b6SeXL6JgQrDaEM0WNneKjAx4aXW6uO0WNnM1bNbdoO/5pQxhr518rc1vv+8A\nVxIlaq0OEZ+DU/NJ7tsf2dM0LdQUNlbWOOzRcKk1NuogiBKTEc/uPmaqhNy2G/a4UFOYinoZ8DvY\nytVe9fy11xweDZKt7Gp3Rny35/8mwzDY2Szy3LeX+Ppfn+Ev/+hf+Mr/e5paucXIdB8f/PhxPvl/\nvJ/3//IxDt0zSmTYj/x60/rvMn6UuOX2+GTegKmpKfbv38+v/MqvcOnSJT772c/e6iX16NHjXYJu\n8Cr7xp/2vRbiBU7NJxkKOveyW5oOD89FKdTaPDW/w2NHh171/lfaDWq6TqPd4cJmnlylyaOHh16V\nCbtmbxjP15ElAU03+OLTKwD8wr3jvLSW49mXs3fXC8DfM93P8k6Zrm6g6gYjIRdr6SrvPTLEerpC\nptLiPfujXNoskKs0WUoUkU0i+wcDbGTrWEwitVaX6WiApqIS8tgoNxVWkmUkSSBbbiGIAhZJYGG7\nzD37wpyYjvBPL24RfjnAMstutl6W+EkW6xzZF6N0aZOiKlNrNMFkwuu24xW7tM1OJiM+BvxOnr24\ngdlokSlWOTTez8J2CbfDgtMsYZFtNBSVjqozHnaQrio0RRsRq4ZL1liNZ5iO2DFLArpJIltuYbdK\n1MsVrGqLP34gxF8sNOjqOl87u4EgCIz3e5iPF5kd9LOWqjA3HNjb60bSxHzXS65QYVBMsib4CXts\ne5/19ezahlZptFUEAfYP+l/zHGm6jtrV98Tbbxc0TWdzMfODSfEX4oiCsDfM83OfuJvhqdA7bpjn\n7cJtH2yurq5y77338slPfpJarcby8vKtXlKPHj3eJVwbfLk2ePPT4FopdS1dIeyxsV1o8vDB3X67\nawGu2tUo1No8t5TmoYOxV733Wtn1+mB4vN9Dq6NxYT3HHRNhTi+mXzWZPt7vYTFRQhQF+n0OMuUm\nh0cCfOm5dT7/vSUkET7x6Aw7xcYN08nj/bvWllNRH7lqE1XTGelzoeuQqbTxO6yUGm1iITvluoxJ\nFLFbTKRLDepKl6bSxW6VqDQVFFVjJurl8laBkNtGudaiq+lYzAKGIPP+I0Ms7ZR54kKCSlPhxXKD\nuyb7cdpkBAOsZolSXeH0Sp6WYaGj6XgsEha7jY10Ba/LgdPR4eJmnsVkmfE+N9lsCbfLwUa+QTTk\nxCRKtJQurY6KU+zS75EpZHO0RCtOi4nNqorfaDExFqWqqBw0t/jCSgOPpOEwRD44oPL3OzJf3lBJ\nZ8vcPTfKRqZKwG3hwkaBDxwdAgGSpSZWs4nnltLU2yqJUgtRLyPXCgiTY9zpc3F6Mc0jh2Kvyl7H\nc7ui+yupMoNB197zrzwDt4vHeUfpsnJpZ29S/Mq5bXwhJ7PHh7jrkX385n94lL5B70/V9vF24rbv\n2VQUhcXFRQ4ePMj8/DylUomTJ0/etPv3ejZ79Hj3cjOmc6/1WlaaHU4vphkMOhjr87CRrb7soS3Q\nUFQS+d0pZrNJ2gssrk2dX5M8emXmdS1d4Zvnt/HYZVodjajPzlDIRbLY4OGDPwhorllYBt02dop1\nMpU2uqbjd1uZjvqYe3lyfc8ik13Hoe1CHV03+NjJfXzuW/MkCg3CZo17hp24zSLf3dHJ1drU2138\nTgvxfAO7WUJRdwNOi2zisWNDqKpOtdXhhbUcrWaLRhe6GthNgCTxs3eOIIgCDovE2ZUcFlniSqLM\nfTEbV3NtAnaBzarB4ZEQy6kyDrOIrCuYbA5sZpmW2kVAxGE10elq5KptlK7G7JAfEQFV06g1VTSg\nT2qTUGScsoi526Yl22koXerVGtGwh/v74WtrXfZFvazHMzxwdJzLV9aZ8pnAZOJyRaLP56TUUAi6\nLBRqCu2ujiwJvO/wIEdGQ5zfyJMuNblnMsCZF68wND5CyOfi+ZUM7z00+Jpn7TsXE9w7vdvTajaJ\nzA0Hbjg/r3cG3i00am2unkvsDfOsXEoRGw/s9VvOHh/CG3Te6mW+I+iJuv8IPPnkk/zWb/0WyWSS\nEydO8PnPf/5WL6lHjx7vEmRJ/Kn/876WPU0U6jxyaDdreS0zdX0waTFJmCTxhrK+IPCGmVfdAI9d\n5gNHh/niM8skSg1KTQWvw8IXnlrm+GR4L3g9eWCAtVSFlzYKaJqO3SyxL+YFw9gTf7+aKGKXTcwn\nSjx4YIB+j41EocETF7fRdTg0EmB7O81fvljkYMzDqE3FEfbQbqtka22mB7xsF+poxq4nuM1i4sXV\nPPWWsttX2lJxm0FHwhA0gj4nSlfjmy/FOTk7wJmlLGbzbl/o3ZN9XIlnaOkieltENjpciueQJRPj\n/V4anS5j/W4W4iX6vQ7Ore3KCW0VqggCDIdceO0WVnYqvGd/P4lig3qrw/J2lQ/dFWF1PcVSUyRk\nE4k5HMR1DdpNni942D/kYtqu4nAqXJhf49FRKztNgeDgIK18Er/LSq7axG230OpoeBwSHzg6xHcu\nJpga8JEqNfZUAgbGx4kEnXvyU6/3oybis+3Zf05fdyZvRvb9VlDK1Vl4Ic7ll4d5EmsFJg9GmD0+\nzC996iQzx2I4XNZbvczbhts+s3n06FE+97nPcejQIebn5/nzP/9z/uqv/uqm3b+X2ezRo8ePw65s\n0a4D0CvF11+pn7mWrtzwGHjDzOtissTVRJlUqUGl2UEUwOuwcmJfH/1eG1/8/iofvGOYlZ0Sgigw\nGnbzzZfieOy7E7krqTL/0wNTPHEpgdtu5vx6jgG/g9VUGUXd9Qt3282IokhX19nX7+X8yg6PHR0h\npFf5blJjrN/Di2t5YgEn5XqbQn1Xn7OlKDhEAbtFQrTayJRbWGSRqYiXc6spmt3dvsZctc2xsQAt\n1WA9XWE07GLA7+T8Zo4JF5yONwnZINcCq0VmLOzmyFiQO90K//XZHWbDVqqSkwuJEo2WimDA5ICX\nWrlCodnlSMTG4PAANUXj1PwOgwE7rVodt8fN5ICPhUSRWqvDeJ+H7WIdr92Cw2yiWy8xNDjA+3x1\nZLOJeaGfixs5urpO2GNnu1BHQiCer/PY0SFWUhUOjgaxvCzifk1n8/X28JXl8dd73btBG9MwDNLx\n0p7t4/yZLcqFBvuPDe7pW04eHMBsue3zaz8RepnNH4GHHnqIu+66C4A77riDsbGxW7yiHj169Pjh\nkSWR4bD7NQeRXtl/91r9eG8kpTPe76GldCnVFd5zR4ShoIs//9Y8tVaHl9az6BgsJUssbBU5MOyn\nrWoU6m1iAQffuZgg4LKiqBpKp4s36KBWb5LpKpSqbYIeB/m6gtsuMNbnolTvsLBdxOW0sbC+Q6Ej\nEQ04KTVV/M7d4LXR6SIIBqIoYREMarqJsaCHlWQBkHDbLFzeLjIe87OYKLGdb2CWRfq9TpZSZcIe\nGx1N55mrKYaCTi4XGlglnZ069PscRHxOAi4LDUXlPz+VpE/u8K0rTX53dJ0LapRfuGeMU/Mp5reL\nhK0C980NU2+02ImniAzH+I2Hpjm9lCEid+kaCqFuCask0DGbODoWpNxUmYx4SBTqZGtdpiR4dkch\nMjbA7KCfpeRuYJ+plDAaVX5mv5/Hh1z8zXKaf/2Bg/Cyq1OquGtJec1e9NoeXr9/hvF/D1iGAAAg\nAElEQVQDJ6BrZ+K1suw3I/v+k0bXdTaXsjfYPhoGe7aPH/71uxieCiO9Q4PndyO3bWbzN3/zN9F1\nnaWlJaampvauX7p0iXPnzt20dfQymz169Phx+UnYBD65kCRZbBJwWrhzIky20toLQhaTJdodDc0w\n6OoGuXKT4bCLZKGB12Hhe/MJHj04SKbcJFGoc36jwOGxIH6HhXytzcpmhruHHGxnClyqWnBZTLhl\njatZBbfDTFPRuGsizEq6wv4hH2s7FYIuG3abiXumIvzdMytMR70Uam020lWOjIeZX93B53WykS6j\nCyI2s4m2ohH0WOn3OUkWaoiiQLerUVO6uKwWYgE7HoeFTKaAWVNINXQEScLpcrBdaOKxyzw4F8Ms\nSVyeX+SPxsvMa17+csuO0mwSi/Zzbi2H3SrhljRqmoQNFYfLTcTvoNJUGAw6ERsVnIEQiXyVWXeX\n6MQEbVXje5cSTEY9VBsdgk4z5WyGn33wMM8u5zGZRBbiRe6bibCerZBPZbjn6DRmXcUoZRibm32V\nw9Mr+yyv77+8ZjV5zes8WWy8ofXoNV75g+PtkO1UO9eGeXbL4lfPbeP22W+wfYwM+3rDPDeJXmbz\nLRCNRnn44Yd5ZazdO6w9evR4p3F9xvJ6Ye9ksQEIqF2NwYADs2x63SBC0+HYWBCnVeYfzm7w8Qem\n9gKPrmawU6wjCCL5nRQCBksrcWKDfcRzNSRD4Px6DgSBXz65j3JzAZ/dTLbSIlVqcN+wk647wIje\npdxtIZu6LJcNPDYTbruFiF9kJVPB67Rwfj1P2GOn1dXwSRZOXU5gM0t0uzoWk8Th8SDn1rJMR3ys\n7hTwuWyMhj1cihcZ6ndTqipkK01KtSYjPgtLZYWA20y11WEz3aU/4EAUDDwuB4vVJlpLo9KuEfI5\nCDqtbKQrRPxOdMnKE1mJ7ypOPuZK8HXbKKuZCrGgnUqzQ7JuMBkQsDr8GALcf2CAsysZqs0OdwUt\nnMlV+TeHbCwLAeL5OoV6m4cPxbi8sE7YYWJ1M4/V4+FvT28Q8zt4dHaQRrvLdy5s43NakA1IZkqM\nSDXGZmfYytVukKV6rT7L66+fPDDAVrbGYNDJTMxHV9MBGAq63lCG62bKdb0erYbC1XMJLp/ZZP5s\nnOWLO8TGAsweH+K9v3iYf/efPoQ/7Lrp6+rxo3PbBpuf/vSnX/P6U089dZNX0qNHjx4/HteXQq8P\nFl5YyfKrD0xRqLX5xoub/NqD068bRCSLdRwWE18/u8knHp1BlsRduZygk66uIwgi+wY8LC5vURWs\nTEVCLK8liUT7OTwWZKfUIF1u8d+fWcXrMFOoKciiyNSAF0Ep8/ziDr8yZaHc1lCdPiYn7Jy6nOD4\nZJgnF3Y4MOCj2FQYDbtZSpaZGvCynK4wG/Pz6w9N88Vvn+eBEReXU3Um+l2kawojA0HqbRWP00Kf\n14ba0RBFg3um+jh1vkm6BbJJRFO7iIZBS5OIBZzoNZX1Qpsxj8B6AVpdg6jPQVvVMEkSHzs5yX/N\nZPlcwktMKrB59DDNjRKT/W4EBFLFJmG3lXDIw0vreaYiXl5cy5Eutzg6FuSFTIV9zg5q3whCoU2p\nXuHEZB9KVyPiknkhbxDxeOhUK4RiESRRQO3qDAUdFGotjo2HQQtirWYYnp5hq9B+lSzVa8kTvfL6\nTMzHYrJEU+myXahz73TkTQeBbsXAULnQYOHsD0ri8ZU8E3MRZo8P8Yv/+j72HxvE4e4N87yTuW3L\n6AcOHKDZbL7qerVapVAo3LR19MroPXr0+ElyfUl9M1vjxL4+nltKEws42Dfge81S+1q6wvJOmauJ\nMndP9VFpdnj4YIwn53e4d7qf711OkK20CHvsLCxtYne7SaSLuLxuwl4H/V4rIBJwWZjfLnJ8oo9U\nsc7Z1RxdzcBpkTju11AMgZDTzEuJOn0DYc6sFdANmIr4eH5ld40rqQoBl4VaS8ViknDZZFbTVSyC\nji5K/Kt7R3huPsG/+8V7+N58gu8vpPC7rNjNJvL1NibRIFVq02q2ECSRalPH65RpqjoPzEQQJZFE\nvkYmX2E6ZOZKVuVY1MaFVJO+gIeJAQ99XgenLieY8wkYDi/nt4qE3Ta6hoFZEhkJu3hpPYcoiNgs\nAvHcbgl+YsBLrtLiZ44OoxsG5UaHRw794HO8miiR3dxAcAeo5zJ0nAGcao1Hxj1sFZtMzE6zlmsy\nFHTd4Ev/Wvu1JyMlCIiC8LoZ62sDQMNh195Q0RuVxn/aA0OGYZBJlG+wfSxm68wci+2VxKcODWC2\nyj/xe/f4ydDzRn8LfOUrX+EjH/nIq67/wz/8A48//vhNW0cv2OzRo8dPkqWdMlvZGgN+B4MhJ6ev\nprl/dgBZEl83iFhJVdB0neWdMoWawtyQH4ssIQigagY7xQYPzkX5zsUELaXDs5c3CQe9hFx2Ah4L\n6+k61VYHAYFau8O9U300OhqGDkpXYyTk4vtXU/z7414SchBdafHU+XW6CHRUA7dFIKWacNssZCot\nKpUaCALNTpdmV8TA4IFhO03RwpXtMm6XjcGAi7qicsdEiJWdCrW2yonJEH//3AZ2i4THKrOcLBN2\niqi6gNLROL4vwnqhSdRv4/hkmC88vcpdfSbWmiZaisqg3cAdDFKotsjky9w/6uSrlwr82rEgZlni\nuZzAJx49wGa2yncuJjixr48nLif5+ROjFGoK33hxi1+4Z4xEoYHNLPHY0WFkSeRfLsTZKTbRdIOw\n20yfVmZbdyOZJN7Xp2EfmWQtnkUvpljHh67vShVJksR09NU9uKfmk8QCThptlVSpwWNHh9+2Opm6\nrhNfzu1Nil8+s4XW1XeHeV6eFB+d6esN87yD6PVs/pDE43FisdhrPvf444+TzWZZW1vj7rvvvskr\n69GjR48fD1EQeO/hwb3g43prypmYj7V0hXiuTlc30A2doaCLrVyNOyfCfOPFLR6ajZIs1pFEkQfn\nomxla5gkgbVUBVEwGI94SRRCuGxmOpqOSTLxiUdm+Mz/uMRkv4dYwM7XzmwyFHKhahpKRyNfa/G/\nfXCO7NoKVp+K2MxTwULErLGsGOiGjMto4nG4WEiUCdokcm2BZlfDIunUO/D0VgOz2OTgaBCTbKLS\n6GAzizzx4gYhp4SmanztTIO2omERBTbrdawWiYpiEPDY8JjbXF5LMR52MBp0cjVR4Y6wmSA1BoIu\nVsoaTn+IpVQFtdnkqFfhy5dVBlwSK4UOkYEwvz2nsVNpcXAkiNVs4tsXttENnVy1zRMvrvDhSS+p\nzS3MngCPXCeuLgoC1VYHp0Vmp9QmLVi5c9LH7JAfObUKSgu9sMPkwQOsXUohSQI7xQaCKCBLwqsy\nll1NJ+p3cDVZQjPeWCv1ZtNVNVYu7+xNii+8EMfltTF7fIgj943xq//+QQZG/L35iNuM2zLYjEaj\n/OEf/iFOp5MDBw7c8FwqleL3f//3+bM/+7NbtLoePXr0+NG51nP3zGIKAUgUGkR8NqaiPmRJRDcg\nFnQS9Tv478+uIiAQdFl5binNgUEfFzfylJodJvpd/D/fWuDRwzEy5Ra6bhDxObGYRGaHAgRcVl5Y\nzdLvsXFuPYfbZqbS7FBpdrh7qp94to4kCgiCSNBpo1xXMIVjbK6sI/rDlFvbbGcaGKKMU28Tb4lY\nlTJhjxWx06StdOl2dwtvmrHbixoJusjXVWxmjfE+L88sprAYOh6LhRdzLZyigSSZ2Cm3sJtFYmE3\nmXwFpdOl1Ohid9iQ7XbOz6/ic7sQlDpruLGVmigGRJwaHVXDbDJ4Li/zoSMRqosXENsaxnqKuDSC\nfUhnM1tjp9hkbsiPI73MPz57mTudddq2IWxmgwfD3Vf4yAtM9HtodbqAwHDIiVWWdgPIyCik1tEC\nUZpdgXS5yeN3jfHUwg6jIddrDvREfA6Wdspkyy2Gg87dyfMDA2+97J3eAF0HXdtdh/TWS9ftZocr\n57Zf1reMs3wxSWTYz+xdQzz0cwf5t3/yQQL97rf8d3u8u7hty+jlcpnf+73f48yZM9hsu+WKWq3G\n8PAwf/qnf8rMzMxNWUevjN6jR483443kaF5LvPup+SS6Ablqm/2DPrZzNUb73MwNB3ZljFQNTTM4\ntZDk4bkYW7kawyEXVlliK1ejqahYZBM7xQa1tora1bn/wACyScRskhAwUDUDXTfIVVuIgoDfbeVL\nz64wFHQRDThYSpa5/0CUp6/sYBgGTaXL43eN8tJ6noXtEoVaG1XTkA0Vh8NBv99BPFtHlkWqjQ6C\n1kHVDMoKhOwiAY+ThqoRcltpdFTi2TpBl5U+oUmiCWagKZjwW0TKXYGAy8ZIn5NStc1GqsSY34wq\nWUkVq4QcMh95YI6nn1+g3O7S77ayVhdQdQOnRaZYqeByWMlkyiCauMvbJjyxj8nONuWuRCwW4api\nJ1XtEG0kSDliVFM7vC+sMDg2zI61j5mh0N4e/cuFOJvZGrlqm/v3R3Dbzewf9L+muLqiahhAolDn\n5P6B1+zZvPZa+IGW5muW0N8smNxZg4FxUFqQi0NsijejWmreIJ6+tZxj/ED/Xll8/7FBnB7bD3my\ne7wT6fVs/ggYhkGxWKTT6RAMBpHlm9uU3As2e/To8Wa8kX/1qfkkGNBQVHLVNif29b2czbSznCoj\nICCKBpW6SsRvJ+SxsVOoI4gCyUKDQ6MhdN3AbjEhSwIRn4P//PULvO9QjL99eoWPPzjF2dUckgD3\n7R/AapbQdYOtXI3BgIN0pY3a1Tg8GuSfXtxiMOCgqxvMx4uUGwpOq4xFlhgM2snXOqRKDQQEmkqH\nRL6BzSwjiBB0W6krXdSuhixKNDtdWs0WuiABOjMhM2XNQkfVydVayKKA12yQrWkMmjs0JDMxqU3X\n6aEtWTCpKg6zxHa5iW6y0NV1bBjMjoVZiWdpY6LVaCELBh4LCAb4PXZMTjdRn53nriQI2gUEQcJk\nqAToMOgRWZUHQNPI5Mt0LTaOW8rYoyP0Jc/TdXgpWAK89+QRZLNlb4+uJkp0NZ3VdIW1TJUDgz4G\nAy6mojdKVV3v9PPDDPS8qb7qmwWTiWUIDUJmE6ITr5nZzCbLN4in51LVXWeel/3E9x2KYrH1hnlu\nJ3rB5juQXrDZo0ePN+OVQcX1AcpGtkrM76Sj6STyu1kwpauhdnUubOaZHfRTrCuM9bkZDrlY2imT\nq7Z476FBXlzLkSzUGQm72T+4G8CupiqspyvkagqJfI2JiBcMsCllnGaRdEUhhxWnzUI8X2ck5MJt\nkzm9lGE66qPPa8MkifT77Hz1+VUais5w0EGjrSGJApv5GlG/nQvrOcyigc0kgSxjl010NB2Xzcyg\n38F2sY6htCgqAj5ZQ7Q6UXWNjqoT9tq4spFHAKwSiGYT/d0SJVsfoDHiljBJEuOjUc6vZYnZuiQ7\nZop1hUdH7SxlGmSKdSqilVqjjUUS8bntCGobq1miiRnZJHIkIBBvSYiVAmG/k7DLzLfW2/jMOjPj\nAxgIpAp1HnLmSTQMVhQnJq2LzyGDN8ix8RCyJNLVdMb6PDx9JcVwyLm3D1b5Rr/6tzrgs5QssZWv\nM+Cz77VJ3MCbBZOaCql1iIyBJGMYBvGV3A2T4kq7y+zxIeZeHuYZ29+HZJJ+rPPc451NL9h8B9IL\nNnv06PFmXD9hPhX9gYPMQrzAmZVdP+3ZIT+FWouT+6NsZmuIAnse2pqu09UMNMPAJIqkyw0MA+a3\ninidZu4YD3NgaLesu5aucHopzdxQgKvJIuW6yvHJEM56lm/vGHhkgUqxSKZrJRZwsJ6pUm11GA26\nMATo6jrTUR+VZodiXSHmt/Hdy0n8ThtBl5VSQ2EzW0NXO0gWMy6LiX6rTkuy8ciQhS+8lMVjNiFY\nLBiGgcXoYLLb6WjQUTVsFgmXxcz5tSwnBm0slTTkdg2sNg6M9rOdKjA1GsFdSXA6K2B0VYYH+9ks\nNAlZDAI0WWpIaMj4LDrVVpdSq8uAy0S9qdJG5GDEQVW0IgkiAaFFX2yAbLVDIl+l3ahhsTsRRIGO\nBnfvCwECQnIZ2+Akic04focZRZBpdTSORKycq+z6v2uGgddhZcBvxyqb2D/oe5VfvZzb+kHpWxBA\nEEHXWBMD6MKNovxvlPEGXhVMvpKuqrE6n9oriy+cjWN3WfZsH2fvGiY2FugN8/S4gd40+o/A2toa\n4+Pje49XVlaYnJy8hSvq0aPHzeLtaM33WuwUG4z2uWm0VRYTJWST+LJQd5Nfes8EC/EimXKThw8O\nspWtsX/wB1mumZgPVdNZTJSQBJHpqBezSeTcWo6A24qm66xnq0iiwNxwAN2AsMfOdy5sU2p1GAm6\nMEkiiVKbTlvgbmuO/zMvMekHQ7cwGHKQyEGq2sLnkMlVFCRRRBTg3ul+nr6Sos/twGmXydfalBoK\nsklAQKCl6tTUJn0eP82GytnVMlaThCQaTLp1FJuXp6+kOOo3k8/WsZoltHaLrVKd90RgsWIQkzsk\nRTfRoBOjmKbUEqgXC6xURRwWkQP7+vjG+R1++wNzWGsFvnFFpd9nZztVJCfYqSsGYYcJzRAIeGxk\nVYlkW2TYrnD30WmElwXXn76ywlTMR0KATLnFYMDFzx4fYirqY2mnTKbiQcmlUXSButXH/FqaOw4M\no7stHNIz5Mwh7vK2+e5SjlwN3nfy0Gv71ev6Xul77fln0PcdR1NbGKVtZo4evkGU/00F2CX5htJ5\nu9Vh8aUE82finH1mja2FNP6Im/HDUaZPjvH+T93HsYPRt+33oMc7l9s+s/npT3+a//gf/+Pe4099\n6lN89rOfvWn372U2e/S4dbxpZugWcn0gvJmtcnJ/lKvJEmbTrvbi6svamGN9ntft2XtyIYmmQ7JQ\nJ+yxUqgqewHm1WSZUl3BJpvwOMy4tAZ3xZy8sF0jp5m5slPl2FgI3dBpqzqzMQ+Wep7T8QYnj+3j\n6lYOP01EX5gnF1L4nWYa7S6iKKJqOlpXp9npYpUlJElgNOzi0lYBr92CjkGprmAxVGwOB82OBhjY\nu01U0YRN72IVoapDswODTuhIFmqKzqxXo+MdoN5sk0nnGe9zcyHVxACsJjgxO4pDAj21ygXFyxAV\nkqqFqZCVnVyFgKyTyldYw0fQ5+HXH5nmS988Q7ahE5Da5E1e7o5Y2OxYmBkKYJZEJFHg9FKGPq+d\nYr3NeJ8br8NClCqarmPCYHJmnFPPX8XT38+zK3k+MWPl2aKJCDVyZh9ho45QzpEVXYwMRxFqecYP\nzr1h6fvUepXY6AiNdIKk4OXBQ8O7ey2WkQUDVe2yKviZiAZeM0CslposvBDfmxTfWMwwtn93mCcw\nGeKR904j2WSeuZq6QS7r7fQ96PH2o5fZfIt87GMf45vf/CZ//dd/DUCn0yEajd7iVfXo0eNmcSus\n+V7J62VXr7edXEtXWdopYxLFPZFvs0mk0+V1ZW/W0hWSxSbHxoLYLSaWljY4NuAgXiijOvwkC3Xu\nmgyTKrWQJZFDIRtJ0Ys/bOHChS1Gw14ylQYbmTozUR92m4XTWxKHInaajRZ3BzXi9JGrdXh4boAL\nW0UMAbwOM5vJAl6rSK2jgmyj3tJZTlfZH/NTa3cQBMhX27i8LrZyDcIuK4IEoiCTKarIJmh34bC1\niuQI0LFaqeQKKDYvOVViazWF3y7h7wuxUqgS9LsRlAa1So0X5jfYH7YiOmPo1Rzufi9Wu496IUvA\naFCsC6TtA1iVFtNunb95cgWPaOAKBmirCie0FPO1Ye4YDyAJAtlkmmGfGafW5NJmg3unB2h2VD54\n5wjPfWcFw+Vj0KYjZ7eY2L+PrWyN2aEApmiQ4doyomjigYDG9y+mSBsOsJoRL18kPHeU715K8PDB\n2F77wu458DGe3kKOTdAtJInqRa76BxgwyXv+6PErKfRgDE1vMSEWkKXdyfdcqnLDME82UWH66K4z\nz2/8h0eYOhzFajMDu33Akk1mI1tlwO+45d+DHu9ubutg8wtf+ALf/va3ef/733+rl9KjR49bwOt5\nTN9Mrg8qr9dTvD4Qviaufv1ksm7slsh1Xeerz6/T57ER8e32dF7zNdc0nWylxbOLaR4asLKmOTFs\nZsZMNTJRH4Xabv/kL9w9yvnlLYJ9NgqZHMOxIJu5BqqmM9HvYbjPxfxWkT6vlamAxvLGCgyEeXA2\nwpfOxHlgNsZiokxblCg3OnTVDvdOh/j7i3lsSptKCwICrGWquO0yY2E3hZqCWZKYGfRSa3YwDIOc\nInCiHy5WZQZklazgxiRKLCZKPDTu5XtbCvXm7qBRyOYmW23TVruUig3mxCxdiwevXgNdxpAkxoYj\nrKey+AWFUVOTpLePUr7JcUcdxedksSYwMeDkPqFM1WTwxHyORWsAp0UiU20xGnISsEucKwpMjUaw\nrqyytLHDh2Y8xNNlxt0wefd+mhtXWS12MEUFxvvdVJsdvvrCNh+9bw57fgsGxhnLVRkzujRKKdbD\n4+xUFO6biezt+Q3nIGdiRpKJBFwsaU5MMjdIJemaxoRPZuX0Ff5mQSG/eoX5s1u0G509V573ffQI\n4wf6X3eY5/qzD9zy70GPdze3fRm93W5z8eJFFEXBMAy+9a1v8Sd/8ic37f69MnqPHrc31ybNn7ma\nYjDoRBC4QQLn9aRvrr3v+gnnU/NJZElkKORkM7vrCvSl02scGvbjrGfpuoJcuLrB4dkJXtwoIgB+\nl4VKU8VvN/H0xU3umRuiqRrE8w38TjMBp42uoWOTTdjMEid9bb6dkZF0FblVpuMKIQgCyzsVHp4b\n4JnFNOevxLFaTOhql64OTp+HcrOD32VlasDDkdEQT1xKUGkoKF2dfLUJ1/0nUjsdRn0W0k2do9YK\nFW+MOb/E1y/lECSJgNfNZqHGSMhFp9PFJaqspOv4Ax5mHF2CJoUFI8RQ0MlmusyITaNfrLPaspBK\n5bDZnFjdNgYG+tjINTFLBq5inJR9gONjQfLpLJdL0O+1sd/VJdm1MOtU+M5qg3/zkRMkMkXMtTyZ\nmkKfVeClTJujh6ZIlhX6PDbMskQs4GSn2GCGHIQGWby8QLsLW3hZStc5ub8fm1ne6699LRmj633K\nRcNgbSHN/Nk4zz25zNbFJKLVzOG7Rzl4YjfAHJwI9oZ5evzU6U2j/wjceeedDA0N4XbvOhzMz8/z\nwgsv3LT794LNHj1ub35oge5X8N3L2+wUWyiqxpxLxSQapCsK7z15iOV0nTOrWQIuGzG/nXKjA7qG\nWsyguQLIZvPexHql0WE45OL0Upr37I/Q6nRJ5BvMDvvYKTbxOmQk0YTDKrEQL9HJpRgNOkjkqzSc\nIdqqQb7a4shoiP1DPr55fpsRi4LerFHQLcQbAk5Jp6gKHB4J0yc3qTZ1Ss0OxWaXlgb9Tgl/MEC2\n2iZbbeO1mWl2NMRWmQmXwJmsgd8CCUUi7LLQaCmIJpmuYTDod9JuNBnUiwiyTMAqkLAOsN8Lz8er\noCjMRD28uF7i5KSPVFWhrBpYHB5MnTpDozFOL2YZd3SJOE28uFEk35XQJQstVePIkB+7Vsfk9jPr\n7GAJ9iGWM+w/fIDVVBUKO7TdYaxWKwN+B199fp2fOzH2AzF2NEito4ZHWEw3SBRq3H8gylKyjOnl\noazrz8G1HxdKS2XxQmKvLL74UoJQ1MPs8SFm7hjCMeLjzsOxXiayx02n17P5IzAwMMBXvvKVvcc7\nOzu3cDU9evR4p/NWJ9xlSWQm5mMxWXqLfXMiv3r/PjLlJv946gInjk0z7FdQk+tsl5380n2TuwGN\nJIKg8vDhYc6t2/n62Q1OTIbRdZ2rqQoTYTfPLKW5azKM22bmxbUcv/f4EeLZGoMBF/F8HbGaJ5NT\necRep9FvsNOsU7V6cWstpkYHmN8qohkGV7ZLeO1morEg21eXqAsW/GYF2emCWgNTu0qtWqNq62M2\nJLKznSRjC1DXJTrpPHnNRMhjo9bs4HdZMFnsPJVs43aYydbqHPRBql5B1kDUFAZsJrazXYKyhsVl\ng1adhO5k37iHfqFJsWPinj6ZJ9drPBiGXL5Etd7C2jdIqVgmq4p4wyrVhkLF6WJpcZt9UR/pnSaj\nboOpfcN883yCTz02x9SAl7WdAqZSmonDB5DNFgSTCU2UuHBlk7mwlc3OAB+9b4KtbO26krQIsSlk\nYG7IgiwJYIDZJN6wz+2aQnUxy+c//wLzZ+OsX0kzOt3H7F1DfOjXjvMH//fP4/bZf6yz2aPHreK2\nDTafeuopACKRCE8//TSw6yb0xBNP8Ed/9Ee3cmk9evR4B/N6PZhvxg/bP3otmI3naqTLTb750hYn\nYg5krcOQUWRLChMLiHsBjUkSSeTrnFnOcGkrz50TIbYLDcqNDndNhliIlzk2GmIlVWEzV+P4RJiN\ndJXtQo2T+6Mkiw1G/RYWKhJf3KhDR2ck1k9lJ03O5iK/WUBpa9QsHUySRKrcxGWV2RZ8DNq6aP4g\nkmwi7BO4XBa4J+jhUrZEslimK1vJVltM2XXiopeI00K9WkVTDVrdNk1Vp88hIkk6hixT1GUOBuFM\nSiHkdRKvqGjdNkMDAbYrTfxmJ4GBGEsLy5xTBQasFmq5IvcPhqnlMqRMfuyhANZaHqevj3Gvg7Or\nWaxmE7MjQaJqmrrdi9XUQhQNTl1Oc2Q4iKbprKTKWC0WxMgIa7km4/0y45Q4tZ3mo7Mxljo2xGIK\n+1jfG+75tX0OiCLP/tPC3qR4ervE9JHdYZ5f+72HmD4Sw2o3v+Xz16PH25Hbtoz+4IMPMjIy8qrr\nV65c4cyZMzdtHb0yeo8e7y7e1ELwx+SaXFOh1ubrZze5czLEXNRNM77KlhRkZiiEquk8NZ8kGnCS\nKNQ5vq+PJy8nQRAYCjp5aT3HPpvCVy7lOR61UzE5ESWJRqmM32jQ6XYRbS4kr5+Qx0E+sUNWk0kk\ns9icLu5w1jjX9uKwWSi97E5UqLX5yN3jnF/P8eJajojfgd1sIlmqc+d4mGo6jclU1hMAACAASURB\nVOBw85g9wf91SWfQ1GLb0seQUQG7g3JXYtIocD6rctAPRRyka23SHSuaKCCKAl6PB7WYoa6Z6Bji\nrq+5x8yBkI1GZgdfpJ+A00y+rXNoZox/PL1I1KQgOd1ErAZW2cSRQSefOVen3++k1lIRRfA7LDxy\nMMZ/+6cX+eCEA12AZ/MyUy6D9095WcrWMYWiWK2WG6WyxCKLmQZDgxE2zp9j3/0P3mBTeQ3DMEhu\nFG6YFG9UFQ5cE08/PsTEbAST3HPm6fH2p9ez+RbY2tpieHiY8+fPc+TIkb3ri4uLTE5OIkk350vf\nCzZ79Hh38creu580rwxmX+VA8/I9rwWl/3xuC90wKNQUDg77ubBZ4ANHh/jnpy7xoYcP85XvLyO0\nqlwsGvgljXZXoyPI3D8gYogSkzP7+Nb5LcqlEjVNRmu3cLicZCotJvq9IECfx4YkCdy/f4D/9LUL\nWMwSNrMJp9VEwGEjXW1yx6gfr1LiSk6hLNgJahXWsk0ODblZ79gQKlm2Syohk0qyLfIRZ4on7XNY\njTYrBY2BsBut2WKf38ROtc3ldJvxoJVi20DUdPwhD06tiSia8NtlzCaDfLFGwAKHjuxncSWB2yqR\nFVz43TbWMzUeOhjl8maetqqztFPmdz4wQ3MnwcTsNLLZwuKFeYb8Vp5ZLjAolHmh4WYu4iBVVXjg\nnlnshW1UTx9Pfe800YOHEcxWxvs9iMD6lfQNto9mi2nPlWf2+BCDzjoixq5TUGT0NR1+evR4O9IL\nNn8E7r33Xubm5vjjP/5jdF3nxIkTzMzM8Ad/8Afce++9P/X794LNHj1uP36Yvs61dIV4vk5X02+Q\nNHqldeV6usILazkCLguDAdfe664FpafmE5hEEVXTMEkSd06EObuSZdAoMbZvjJWrK3wvLVBqdpmw\ndfjnxTIhh8REwMJaw8T//vPH+PNvzaNqOqWGQsBpxWKS8Dgt1JoqfpeZWlPFYZVJ5OscGQuSKNQR\nRZFkvk7IY0MUBfLVFsVGm6NBE6LTQzWfR9FAszkxiRLmfBwrKmhd1GaTnOwmIHRI6TZUZwBdkJAl\ngU5HQ++2qba6DEb8rCbL3BPUKVv8WPU2gXCIrWSGEaNMKBoh27WynUjx4YePcmGzRDxfZ6zfjdtu\nxiQKZCtt7trXR9ht5aX1PI8NaHt2kaqq8qUz22iGQcjvwtOtUI7Mcc+YhxfOLzM8OYaWS9BxhBGK\nCi+d3uCl0xvEF9IE+927to8vSxH1xV7Rh7uztucURC5+g9NPjx5vZ3oDQj8CNpuNxx57jL/4i78g\nGAzyyU9+kt/93d/lM5/5zE0JNnv06HH7cC3I3MxWeWA2ynKyxKn5JENClXGfBVnQ97JcugGxgJOo\n38HSTpnVVIVMpUmy2CTgtKBkk5xa3yBX6xCO9hP2Omh1dvtEzSYRw9gVfI8GnEiCwHahzuHRIM8v\nZ5jod6NrNp4+9RwnZwe5mC7TH/HywnqBqFcm6pRpyi7+7Qcn+NunVgBwWc0IQCmX54Cjg1HR8Tj9\niAQRRAFZEjgaFtmKp+h2dYqGjN1qoqsb5MpNmkoXsyhRMGx4axUSbZn7DgywU2zSaKs8EFT5Zt7K\nSHOdtGuYX5bW+Jb1MFbDjFitkTGstBUNn9OKJpq5I2rj+VSVoFnH43GSSheJjMZodDSORV2kqmau\nVgRMtRSH5yZpd6HSUvnFGTvlRosriTxH5sbJVtoM+Ow8t5TmoYMxeFkXk/hV5FKSvso60YNHcTbz\nfHFb4Lfu9bBycYlKRuD7z7/Epee3WF1IMbwvTPRAHx/5jRMcOjGMx+9448Oga7uBZmYTohM/9bPX\no8et5LYPNh999FE+/OEP8+Uvf5lUKsXv/M7vAJDJZG7xynr06PFuQzdgUijSUGvMn8uTkXw8fHgI\ndbvFU0ULw14z2qUrjB+cQ9N12ukEVxO7dogTh2Y4v1kg7LZikSWeuFDidz56H99+YZV2IUvTYSNf\nazMV8bCZq2MYBn1eO8lCfffeusHn/mWBoaAdA4P3HR5iWB9hyxTi4LSbldU4Ibedj73nIP/lf1zm\nZ0ckvnbqEoJhUFMkPHYzTVXkgF/i7plxNusC+e045UIRTyCE32Uhu1NidHSA9Z0SP9PdJNMRuZiF\nZkfEaZWpq2ANOgj2RVgpbGKU89SLLfw2E6fyEq22yjlxEIeic9a7j3xDI1et4Qm4EFsag25IVWq4\nJLjUEpkNmLHIFq7WZe6aG8eaXKBlcqKXKgQH9lHfTPLgew7RLOS5sphn0mkiaLHz/13p8DNDFnJX\nL/H41CBnl1M8FNax57dYW1pDV6xo69uMv+d+1E0V0ik+c7qNo9jif/2r/0at0GZwOsyRe8a4/+N3\n8IcP7iPbUN5a60RkFFLru4Fmr4Te413ObR9sbm1tEQqF+MxnPkMgEODv/u7v+PznP0+73b7VS+vR\no8ct5q3KGL0Zmq7TVFWE4ABSVyVWybCcdPHEmQya2Y7iVDlx54G9ns/FTAKpb5DpoAU5u4mqwsHh\nAF89s86k34LabKBXi4QGY+SqbR45FCNZaBD126m1VHaKDe6bGSCer3NhI8cv3TtB2GPjy8+vMx8v\nktusMjjsIrOdIKlZ6fPaeOJygo/eM853zixhcXnYTJc5MSBwqdhE1hSGrDWE9YuY2wIOyUfZ6uCF\ntSwHhwNE7Caeu5rCorX425qNuaiH4TE71pVFOrZ+6oLB5uo65R0Zo6XyUkLH4XQhtUsYZhsTWo4Z\nS5EnLfvp6G1i1i4NyYlJkjk47OLCapZpL4Q8Lp7drDHmt7BRVpEl2Epk0KoCCUNkn8uLdWOZ6YPH\n2C4rUFf5Vz9zHJQWT33jX/jIyAjWzCr7H3qMnWKLx5wV1kpd9GCMjW6Gfd/5MovbJv72L5aIL5cp\nNQzGZwLMvm+On/2fhzD1OZke8rM6f5UJvwW5kSAQGYW3cj4kuVc673HbcNv3bAKUSiV8vl2pimq1\nytmzZ7nvvvuwWq0/9Xv3ejZ79Hj7cm3I5ocVWn8zVE1n9eJlJqYnkPNxloQQZ9cL6N0uB5wKDXuQ\nlUyDjz84tRvYJpYhNLhXar2wXWErW0MSBYb9NpKbW9x9/AA75c5eVm0xWSLic/Dl59Y4PBJgYbtM\nyGPhu5eSmE0i9ZaK1SoRdtkxdI3/ZdZG3BTkQrxMH3U2kjmWihroBo6Aj1q5Rl4ViTlNHLEUaYlW\n9FKWnCWI3+chZmpytW1jsybw8fsneGl+DcntZ2djk4Yu46KNXWshDYwiVXK0NJgaCnN+PQPtJgGr\ngOANkSw0CCsZ5o0+LN02us3BvtEBYn4HQbeV00sZasUiXhS2WhL7/BK5loDb70MvpQlaJTzlTXK+\nCaJiA8PjZ3z/frq6gamQYG5uCjKbrBXarJe7qIqC0agy6BKxGE6ePpMmm1B58Zk1RJNEdNTBA7Ey\nR49FGLr7EMsNE0NhNxuFFvv8ZmST+P+z96ZBbp3nvefv4OBgXxpbL0A3egW6wWZzFSmJkihRuyXb\nsR3HznZjX99M5c6kpmomuVlmKh+czJd88NR1UpWaiSuZiaecxPfGcWLZ8m7LkiiREneym93sBhqN\n7sa+7+s5mA9HJEVttmRZi4XfFxRwFrzEi2r+8TzP/3kglwDXGDhHoZAErY5ItoEiy8jOMWZ9rps/\nUFLRG7WgA0PQgPczg5rNN8EXv/hFPvvZz3L58uUbr/X7fb75zW/yhS984V1c2YABA94rvHw++c/W\naP2NkUQNutFxttbDyE4vfUE17CzvFNjo6KlUqtwRGGErU1WjqcOTSJmtG6lWoyRSbXbpKX2mhq2M\nzczwzaeXoQ9nzssM+0YZddl44sVNvA4TP1lJcP/SOCvbearNDgBHZoc5E85y/6ITvaThq1d2mBhq\nYOnKnM+2cJmt3LbHzvcvxviccYu/jNsRBdhv7bBRMaCzu9hrqzDnHeL/vVhhZnKYbK2JW+oRzTco\nCGaOjti5uK7jk84cisHGi90plvp50r5JKrEYl3aKdJsNCooRh9XMRqrOR3VRntdP4G41mHTraLlH\nqDY6eOeGKSQSfMivpemx85PL2xx366n3ZGRRw1Fjnmd3axiMFjYUF3PFKH2Lmbw4w3hPQRI1LOwL\nwerz4PTSzqbx6Cb4929fI7mWJbXdwGg3EAwNcccdXmZOHGNxysJkYY2ENMfUgb1w+SfMHv804asb\nBIfNSN0mXLsEswfA5oarp+Dg/ZDeRnGPE3DqaCS2CCe1N3+gKMpNQ1BycxDVHPCB4gMrNre2tmi1\nWvzJn/wJCwsLN16/evXqu7iqAQMGvJd4eaP17Wz1bUmpK4KWwIH9NNo9vvaDcyyNGsnHK3jGRvgP\n984Tv7ZOQKen0ekSTrkI+W+KkkShwZjTTL3VodTosJEsowO65iHC8QIWXYbzpTalRocjgRFsRh1n\nNtKUGx1mRuzMjlj5zsUdPDYjuUqTp1eTHPJIDE9NkkwViV3dQOnpuRKvMKTt8y8ZOz4LHF2cYmV1\ni1xTZqyfwzjp4x+W69jMEle3ixwcEbE6PURSFexGPT84s4GhW+dkzUatKuEwNbjWt5Hb2KbSgaFu\nnmFdD6/DTK1Y4J5DS6yFodOQKSg6eh0jSqHJlNvKtUSRwxRJ1IaI55voDAautowokoFgO8yTiQn2\nmnts5xsEHBJTssyqxctRIc5m1sSw2cDFb62yfDLK8vpV1tcKTPguY93j5+4PBwkcCxGSI2yLHtKp\nLA69CaWSYyvfYM8hBzz7r/DIZ5F0ekJuHXSboNGAPwTdNqyfgX3H1UilIiN3mzR2d4mKHoJj9pdt\n/MAQNOCDywdWbH7xi18E4Ktf/SqTk5M3Xo9Go+/WkgYMGPAe4/ooSXj9yUA/ta4zFb0ltdpDw8p2\ngXObWXo9hYRg55P3TZCJbWPSaZFlhYZrkuhulqAmD3hu3Kqn9PHYDehEDVe3ixwNeDh/KY/SbtNu\n1Hgha8bvkbHqtVgMEl/6wVVG7Sau7hb4yG2TfOfCDvsnnUiiyLOrKe5f9DIn1RB7HfStEh8+Ns/z\nV7Yw6UR6gkixC3eNaVGSUdqinj0+Ldlyg39eb6ITNQiSAZQqL+x2marsMG/X8KvzPk6JJjK5Nrme\nxEQjhyhbKKKhrzdweM7DyrVtbpsRWG1ZuM0t0+wWiHThE5Mi30mAyaJjt9PHoBfp92G30kMZtTJq\narKVgckZN15NjWfyVh4/Os76mRT/ZapEt9VgWZmgcmWXp/IKF85dJpdpsRCwsndG5Dc/7GTu13sk\nRueZnJ7kmeVtQt11dusCC+4Shl6eKX2a5fFjiIDUqcHDn4FShkhNg4ILOXKW2aO3I+V3QNCoEc3r\nKfGxaWYvP0u47yLoKiLhADQ3jg0MQQM+qHzgazafeOIJ/viP/5hgMMi///u/8xd/8Rd8/vOff8fe\nf1CzOWDA+4PXmwz0U+s6ExE2BPeN1GpEHCFZqDPhttBOxmiZnOSTSR4+fgCpkKCb2CTcszPn0iFN\nhW4RJsvbeZodmWSpjqFeAkXh6UiRBZeOlaqWhw/6uRYv4bEbiWaqVBodfvOeAN+7sMPV3SKfuX+e\nQqVFodZibtTOha0ckgbGW0kw2XEpFZ7Y1WDut3HSwG40UqvXqGosdA0m6AuUWzJeh55L8RrVhoxo\nkBDlLiN2Ay67lVqlgpU21b5EPZ+np9GiFzWIGtjrdxGt9JDKWfSaPrKiYDNoEUQth4e6ZCotnlPG\n0Vss1Gp1PnnfEk8tJwkYmvRrBbQCrLdMfCho4UuXW/zxYQPPn9lmZ7tLIZJnK9qmWazhn7Uwuc/L\nzNE5HvIWMY1Pwu41iFyC2x6FcoqI3k+n0yVWlTnu1WPaucxaXYd/0kt0ZYXg7Xci+YM3IpEbmYa6\nz40msbV1Qvv3vLZoHPTPHPBLzqBm8y3wta99jeeee46vfOUraDQaksnku72kAQMGvAd5vdnlr6zr\nfFUz9n4PWbmZWl3wDSGJAq2OTNs2TGZ7hwfv3sd2oYWSaXCm6sXdKRKr9Dg+IWB62TCzeZ+Dv/3e\nCrLSZztR4DfuX+J3AxP8/feXObJnnHCqzLDdhE4S+V8+vI//+sQlMuUmqVKD37kviEESETQCi34X\n5yIZtIUElVoTkSRtp5/LDTg4OsT5RJ9jw31yGhteh5WAscW/JbWUak18/QpK10lfUTCa9YyZgEaX\nWqVDrVHEZLczbYLnd+oY+11GR93slpqUmj2u7uRJKUYmjDY0tJiyaTFWc3gdFlarEtlKGyjhVIoI\nejc/fvEaQr9PxzFMOVdkNBDg7naPv/3yeRxtDX/w11sg99i3NIRv0sLx37mbGXeXXinDGWGCGXuX\nbcHFrH0E6cXvqkKzL0MphxI4hC56Bd94iGdicR6oFJn1+AkvLxN89CNIjbIqFl+KRN7Y53yT4P6l\n13eeD9LlAwa8ig+82DQYDAwNqYX/7XabSCTyLq9owIAB7yVeniZ/rT6KrxShr2rGLjqZ0+QJi8ME\nX3Inz47aWdstYjIa+ND9R9TrSh0CTolwS8OExcjw7BxPLyf40CH/LWuwKw3u8Fs4W2/z3JVtTP0W\nf/Sp23l+PYfZYyNVqBGNVVmLFyjmi/zr03lmnBLZnW10WpFxpUi/58YvdnkwJPK9HQu9kh2xXkTT\nEak2zdiFNqtNE14LjDsMdLRmFl0dalYT4bqFvZ1dDtx3iG+d3UJnsaBtVxF6XY7oSlwptPlObgjb\nkJtOTUMhkcQgScw5NGxrnNjqHbwmSNR0nEx2ecBp4EqmSRkTGusQvUqPXayMCy1qshlzsca3v7OO\nvtAmGT6H3a7l0OEx9h0d5X/8pIOSTmTy0GGiL5wiKC6zpd1D6N4T9C6voR2ewO+2EF5bJzS5ANsr\nUC0S8d7O1uYObfMME+U8d96xj/CLVULxM+p5F34I930adMYbLvLZXo9wundjD1+XsWlYPglOLySj\n6vPs7sCJPuADzds/uPd9xiOPPMLc3Bx//dd/zeLiIp/73Ofe7SUNGDDgPcT1Wk2/20o4WX7V8et1\nndcFiKwo1FtdVuNFNILAnM+FNLlAyO+5cY4kaliadLHkd95yXcM5QTKRxjE1w6lwnnv3el+1hnS1\nQ1J0EO47qefSHFyawWQ0ImlF+opCvtbhf3p0L71eH5dJy30BO42OQiSyi3bISV+WSaeyTJgFupkE\n/XaTrqLQFY2MDZk4PGFBNJppNhuMNeP0+xri5S5epUC9I6MvpYh0zfy3H1yiXGlTTOdZsrTxW+BK\ndwiNwUxALHLnlA1BI1DQWvC5bIwOmVgQizR60OxBo9lA0IgUrT56WiP5WhtncYfdSJ3K0zGe+dJV\nnvn80zz7j8u4NAJ79xv4X/9ghIf+4ACP/8c93HdIoGGSoN3ka//8TZRqkYgjRLvTo/HME+zEs3hb\nKaKZKhqtxEZVYK2hp7v3XpRaifvsZWRBQwctLzxzCjIx1hxLdDUiTC5CZvulL4DqIpcEhVDhMlIy\nDHL39b8wogQeP0zMq22rkps3nejXnw8Y8AHjA1uzmc1m8XjUwvtSqcTGxgYzMzO4XK53dB2Dms0B\nA97bvLJW86e50ruywtpuEQopFtx6tgtNFJcXWVAjmq+8/vrzdlcmnq8xbDdycSvPJ+6cwW7UEVlZ\nZTPXxGPSILjGGO/k+betHm7qPH7/IbqpHWKFFigyZ8o6dgsNlD5kSg0C1j5aDTiNWiyiQqqhUMpk\nmVkKMd4vY9EKKPEIZ/ujRHItZKsDBJF6tcrH7g7xwsV1FkeMTA3p2ImnedBSZNkc5B/X2oy1kjTM\nTjaKPQydGv1uF7vdgkPToy936fd6LIxZuccr8OWogTvqV7jqPYatluFqU89t8z6unL6MVetg/VSY\neKRKp9DCMmJA8js4etiDVqpw0CvxfFHPb9oTfDVt45HDM8iVAqJOwuDwECivcSVeR6uFyb37iYSj\naKcXmQzMEFtdY06qslWBgFWhIZqJXbmEMDqF3+dho9JHI+nRagRC1i6Nq2eJaYcJLcyAf0EVjtd7\nna48B/vvhV7vp9divqI/KsmoGtXMxcHlhfHAILo54H3LW9Et4uffSTfMe4gHH3wQvV6PzWZjbGwM\nn8+HyWR6x9dRr9cBMJt/yhzdAQMGvGNEUmXy1TbZShO/x0osWyXwUpo8X20TGLNj0ktE0xUqjc6N\nc+1mPdvZKqKoQaiXcc4FKXYEApoCJs8o0XSFeL6OzaQjkioTzVQpNzrYTDpkWWEjWeGB/eOExh3s\n5mp4bEbya8vYhmxsbcWJXAvj0LSYbOxQ1zto70a4HMtzwO/kakNPKZMl1RRwWw0Exuzk23BAXyAp\nOnloUuJ7kTqPLroplGrEKjJToSD/FhfZzVaoo6PVajFkNSG1K6zvltA3CgyPjxOJxrlSMxDNVNiN\n7UCvQ64jUFdE2vUGZr2e201ldutgFhVEpQeSAbe+jz4Z5kEfRDom7mzEaIQLXHoux8p/v8D6czlK\nyQLeES0TB4aYesBL6IAJZr08GNAwfuR2CpENPmHc5lxeg0vX53A7QqbRQ7TYie+mqLtnOJOROXzf\nPexevsD8uIMR9xBSLY/HqkO0OsjKekzjk0QvnEeUdPQXjnB5K88hr5nxIT35Uh2TRiFaA9E1SnFo\nkmy9i92sR6wXIbYMsgzOMVVoemdBI6op9koBNi+rbZCqBTDbwOpQhaZ3VhWVZhtsrcD+42Cyq8ds\n7pvXl3PqORrx9b+Ub+bcAQN+gbwV3fKBFZvVapWFhQW+/e1v8+Uvf5knn3ySWCyGTqdjdHT0HVvH\nQGwOGPDe4+WC8rrDXNQIAGQrTVVoZioEvEMU651bxKcgCOrzZoFoWaafT2LyzxLN1Ql4h9hMV1mc\ncBJejzJp6LG+k+XCbvWl+9i4ulNkJ7KFtVfH0SmRz+Xpag04Kzt4dArahaMotRKn8xpKikQ5X2C1\npFCObdJz+rgr5CVbaZGttrh3r4+CaGXR3GYDB33JQFMyoVO6PHJ8H0+fWSdXaTKu79E22mh0YUTX\nI5prcsyQRe52ma9tkBBsBIfNTGuqpMzjtBpNch0RZ6dI12RHJ/TZaOrwWAzo2hXu0SawahWknV1W\nVzs882yNH327xA9PVpDbHaZ8Inf+6j72PzyCNDfEQw8FMTe26YtaDi1OY6qlabR7DMtl7hQSmEq7\nBFwGotU+p8t6kroRlHYbRe5STac4fmCas1e3ObJvFmnPUaiXVDE2MQ+1Enavj+j5cwQeeISyLDLf\n3MKraxPrGvHMzGLvVYlW+wTuuJOyNETA57ixnx5Ni4h1jrw0RPbKOeyLBxElnfpFqRRUQdmqq4Yh\nj18VkkPDqpi8Lgg1opp6N1pvilCNePN6vemmAH093sy5Awb8AnkruuUDm0a/jizLPPfcczzxxBP8\n/d//PbOzs5w9e/Yde/9BGn3AgPcer9fmCF4aOZksMycUkYQ+a+k6/vmA6lJ+yY3ud1uJJgsEhQKM\nTRPONG6Yi753cZtOr09qN84nHjjEE6c2GNY02bNvgR9c2mXCZeZBT5fu8BTPL2/jK6xxOt3no5Pw\nrbSBfcYmF4sCntk57pSj/F1E4rGxDhG9nzPhFOYhB/T7/N6ji2xna2hFDQZJJCAU+OZKjqeuZrlr\nzoFeK5JrdEnIRoRakfV8hwm7lrJi4JgujW9yguW1LQ4bSnw178YlNClrrRzyWfiIYZfvikGy4TB7\nnRqeiCmMdUukShJCokIkIdKJV9AatXgmzcxNCBxesnDs4Bi6ZoWu3ko4ngONyJzPxbLigquniGNj\nxCyi0WjIzh5jsrSOUC0xm72IZBtiVXGyU+pxu63BM2UTktDnSNDL6e0KD+8dRRqdfHWKWu7eNOwI\nAms9G/7sClHPkjoNqLB7MyWeirKWquO3a4kKToITbqRkmA3ZTkAo0HBPESu0bra3up4uv/Is7LkT\ncruv30dT7qr1mmMzN49f+gnYPWp6fd89qiHp9Xhlan6Qhh/wLvFWdMsHVmx+5Stf4cknn+TZZ5/l\n8OHDfPjDH+axxx7D5/O9o+sYiM0BA95efmqT9Z+BG4Lyuvv85XOtBUFt5p2JwdI9dNsdwmthNCN+\nNBoN7a5Mv99n4WWmoevXR7INThW1ZKsd2uUSNXRY+i0+vd/DDzcrfGrRwZm6kZFemR3ZzISmwtL+\nEOXzz/C84me8vElaclA3u0nG0whmG8XdXQLz02xsRHHYrez3WrhclRiTOoj0uW/KwvM7NUZ6ZX6Q\n0fKf9ln50sUaqbbASL/C4mKQZ8+vc9jawGKx4FGqtLJJ1lomjggp0kPTXGzZmB+zYqolOV/S8z/P\nNFjOazh3IYO2KXHqQolMqofJZcTjkdm/10bXKnL/vJEeGnp9AUamMCTWCPUz0GuD3sTaUAh/bZuo\naZJg4jSMzRLO1pkTSmzteYxAaZXGWIDY+XOEbAJrFYWWYQi5mCGhWDhmKnFaGOdE0IHJoAP7sDrd\nZ3Lx1g19We/LbipGON++MZ/+FuGWiNAdmSa8nWFOU0KaXAC5y9rlq/jngzd+UNzY1+sCctivGope\nLiR/FnaugYAqhAuJN64DfS2xOmDAu8BAbL4JPvrRjxIOh/nTP/1TfuVXfgW73f7TL/oFMBCbAwa8\nvfzUJus/I7eIViWPNDGn9k9cfR4OPACbV4A+aKVbm36/4n0jqTLbG5v0bG7kTodMMo19dJSnrsQ5\n7pNoGYfYiWf48N2L6Po9wpEY49NT+OUcp8pGju+dUKOkxWUifRddBXax0izkuRiv8ZEFCz8O1xA0\nGoY8bnLlOgGLzMKIBcXpJZ0rocQ38Fm0hJs6xnJrfFtc5BNBA1mtnSfPbPHZwy7SNYVEuUk7vkWp\nJ/CIu8XVVJ0Rj4OnKjamawm24wquaoflcIdUoonPp8M93OfBsQym/QucLIrM2xQSsgmcXg4WzrIr\njSBYnBQVDRpRhHyCYW2P8WacmduOEGuKzAllpFKCSEuPgoAsQ0+UmDl4Ofj1YAAAIABJREFUkOjF\nCwQf/jDSD79M97bHWIvsEL+2jm94iHi+xvGlCUylOBHf7SilDLJGYnZuCmn8DSKIova1hdvrRA9f\n9ePj7WIQrRzwPmQgNt8krVaLH//4x3z3u9+lVCqxtLTEY489xuLi4k+/+G1iIDYHDHh7eaMU+Jth\nI1lGI0Cj3SMejvDAnYtqJIw+jExDMqxGOMeDIEq3vq+mhCT0QZHZwAW5OL7ZaX54apm1mg5FEMiV\nW+yfcWHQapnTVrBIAoF+josdBwntEBpRx+iQkVSpwaTHipCL49+zwNe++wJOuapGUT3TTLlNxLZ2\nSaWLeE1g7xTZkLz85kEXzxSMPOBus5JpEC+2kWslMsZRcskUdx5d4utntvnfP3kIa/gFnl7LQrOC\nu1chLrnxxMOcjluJ7/ZIJ2WKZZmZKSMLCxZmh5sQmGGmsUlHMhGXPEwnXuTbmgV6ooFJoYRX16Wk\nMeFbCJFK5dBKEj1E4okM+8UcWrMNw9AQoV4CHvkska//f2ylq/jcVtqOURItCUHScXxhBFNyVe3/\nNLsfUltsLF8lsCdIwzxMLBojdGCJjVSVQG2Dxl2fIpYuE5LKNyKFkUuXUfp9ZKuLWW1NjVi+Fj8t\neng9wp3dAdcYIPx8fTMH0coB70MGbvQ3wcrKCl6v2sOuVquxvr7O3/7t3/Lss8/ye7/3e+/YOgYG\noQED3l7sZj3RdOWGe/ytkq00aXdlNBqBxdkxYuthPIF5sLleikQFwDF6wwRy/X1FUUM5XyBrHMbu\ndJDf3qJpGyETi2HzTVBudjkaGCWer1Np9BgVmwwbRXbWN6hOHULWmWjkMjx4+wIOs56dXI07giOY\nmgX+7dQG/XySrb6V+6asLO8UGZY6NIwO1tI1PFKPK+I4U0aZc8kW5WoNcdjHjzfrZNNZhmfnCBg7\nlAwuNjc2eXCkSye5TTmdpbkVJ7pc4bkXu/z42wVOXhTIdPWILgOPHunxa/f1+J17tLiHGjT9c+zI\nes7nYdLrYr82x4riwTXixlyK0+t2kMdDPNpdxqfrIiuQxURX7lNMp0lpbCB3OdjdQpoMgqKQTySx\neIbxaHv8JCvysZE6veARVi4uE7XM0u7I5Le3sM/Mk7d4MfU7RLETcIiIehPZ8AYml4torkmgEUWc\n3XdDwOVTaQKhIKZKiqjowWNX/95GUmXy0U2yyTT2dhHR5iDS0pOvd290F7huDANuNQRpXmYIeqtm\nHY14q5FowID3AQOD0JsgGAzSbrex2+2cOHGC++67j+PHjw/6bA4YMABQU6c/urzLnfOj7OZrapQ0\nG3vVJJhX1ohuZaoE5DQNuypQ55b2sJasgSCw4Bviqyc36PYUNtMVbpsbZpwqWcnOg5Yyy9kmWq2E\nNDqBf8RBNFOhn0sy45A4uVkklc4z7HEyOznG//WdZe4POshKdg7MjKDdvMALiQ75JujMRu45GiKW\nb2LSaUnt7lJu9BhrZ7jcHeITe5z0wlv869kW1Uie9HoOi03PRMCOw1TGe2wfydwOv318lqfOrFOT\nbHxGcwVJUFgbuY0XCiJuo5bNssyotskeqULfM0ErE2er2KHgCeCv7yA6hxkTW8w8/BGeem4ZV7/M\n5UyPI/VVUm2RnCdAv5DioLHCj6pOpmYmWd9KELQLzB47zrnVbT41VuMZYQ5NIc5wZQdx/3EWyquE\ny33mZsaRREDU0k1uEd6IMecxIx15GGqFG5HNte0sfjlHVHCppp+Xz7Xv52j0+sTSFUJino3hQwTG\nXWo5xNo1Qh7Dzf1ORl9mCLpDTcu/1fT3y+uAB1OFBryPGKTR3wS///u/z5//+Z/jdr+77SMGYnPA\ngPcur6rVe5nR5Hpj71fWiAoC+B0GoqvXCC6FkHT6W+75/Us7TLgsrGwXkLQaxvslUn0LU5oS55Nt\nPONeRp1WxFIGXTWNUsxxBi99o41cfJd9i7OcuRRm31KQD98RIB9Z5x/OptE0KuRbajvIh9xVcrZJ\nyi2ZTz10kP/76y/ibXc5e3KLVLRKLV7DYNeyGDDwwFEXM7Y8tU6DkFRmw7ZATD/K2ViJUk+DXavw\n0IyF/eUrSJJIN5/mrzX38FuLRp4tmdGlwzy2dxhqFdaqInSapNN5BIsd18IS3VoFi8vDXGGFsHUW\nuZxnhgL/krVzh7nKalWkYBnDWYmjr2boGIY4p4wwLRQJ7Algbha4WDPyicUhwiUZXa3AkiYLgcPQ\n7UD0MswegFNPQOAgGKyQWIcHfvvWmsvlVeaceiRBuSHu1uJF/J000WydoMeINDLO2rUo/j2LajkE\nhZu1utmXDEBvxhD0RoLyNb5LAwa8Hxik0d8Ejz/++LvSxP2VDNLoAwa8dxE1Ah6b8WYqtZy72efw\npV6J2UqTdKnBle0iVqOWqREbsVyDQGASSXq1ENl4aY76j5bjOMw6YjWFoKHFhZqRg3umOGhucjmS\nppHLUJHsHJodRZI7JFNZ9h9a5HvntvBN+mjJ0OkqXN3KcGApQLlYxdIuIRkkfhjTEF8rkD6X4l/+\n+hnWnwyzGS7hH5YYvWOWzz0s81v/x2+z0Sywd1Rho6fngKmONDJBNpXlaqFDRHDT7ossjRhxZtao\nDc/iiV9BfOA3MNXSrEQzTFY38Zq1lJ1T5IsVgg89TmNrnVXcaBaOouk2yVZa3B4YRhpyUdnaoN9p\ncdl1GF0xjs5kJlqReSRo4yebNR4dbvOTsoXHR1oszU/x/GaJSe8wI9kVil0RsdMgdO99iMW0ug99\nRRVzdhfUiuAZh9XTMOIHQbzR/FzUCHiUKuL43M39a1SxZ9aJJvIEGptI84ehkMQ+EyCarRPQlJBy\nMWjUoF5QyyZESU17X3/8aenvN+qN+RrfpQED3g8M0ujvQwaRzQED3ke8hqGjKys8tRznxF4f3Z5y\nw4n+8vS6IAic38ySKbcw6jQUa20WfEPoJS0uix5LI0c7l2a73EVAIWfzc8TSoJXPIFqHSG7HcU1O\ncSnb4bDPwpVUE6VRIdKQOGxrkVZcxJ+/QiZcopDs0ix3cHv1+CdEysMO7j3sQlNOUJw+ytbmDvtt\nHZ6rO5g3tdFpRR7zQ6HaJtSIcq2i8HdFP259H6lZRRj2MkuZD4kRJL2eiOCho/Q5s13joLlGSnRw\nnz5Ft9Mj5lxAW07Ttg2zVpCJtPQ84qyy6DEiNYtsjN9FYGGGxupZriluLkTSLFq6XK2KhJQEOcGG\nkt3h6PwY3wy3+NSJJeydIl3PJGFliLnUOaSRCTV97RxVXeUaEUan4cKPVPEpauHwQ+pYybXTal2l\nIoPSU41d153f6Zdmn7u8sL0K1Tzsv++WNkj0FfVRAA4+ANldyOyo93KO3hSgr8cbuc0H5qAB71MG\nafT3IQOxOWDArbwdfTJ/Efd6I17LAX89vf79i9tc3S3S78PdoTFkpc+VWB69JBIad/DUcpz/tGhi\ndyeOYrSRCm8wMTvFmN3AczsNpp0GNKLItsZBPLrNgfo1/nvMyXStzJWNNpnNCkqzxfCEHvOEnWDA\nTNkAZqVBRWvD3m9Av0/d4edDUpTnmi6qBhfGWga9y8Oj3j5fjcDjtVPEzX6UXIJLuilGOgU2GjrG\ngnN8tn8Wk9AnUuuz1dLhE+o0O10Mw+O0d8P03ePo3F6ChjqRSp+xwjp/35rnoRkLcrOJWE6xpK/x\nT+05HHqBpOjgV7mCyWTiv9XGORwYo71yGlGRmdXV+VrKzMEPfxQpscFsP4dkd0K/r/Y41YiweAzy\nCVWowa2pbUW+KSqLKVVsXheOohb23qU2T99dV0dMKjKIIkwsvLoNUq+rGoHcPigkQaNVj7m8EN8A\nnX7QG3PAB45BGv19yCCNPmDArbxy9rjH9gZTVd7Be70Rr+WAz0YjmJolXlhPcbdXT6PR5IVrCXQ6\nHVaTnplRG5lyk8cO+Tm7toNHqaDvNij2NMx3E5yW5njg2F4aGjPnvnWSzKkw299Z4V+eqFIJl1E6\nLfrjDu7/1SDO+6c5sk/PHR86hK4c5eP7RlguyNQNQ9xZu0LGMIqlmsS1sJ9vx3o8FrRyJiNzaKjH\n2Z06d9uqLI0YqfdFKi2Z6U6cS4qbvWKOXxuuYmoUQG8gr/dgqSTxjE/wVEbL7a1Vylor4swS84lT\nSLYhLl3b4fvaEMW+kalaFLnZQOo0GbFoSbY1TI45sRe3eLZiwzwVpJOMsd9QIVNqII5OMm6QMVks\nhPweTDuXiRp8eGJnVXE5PAHzR1UROT6vCs/rju7sLiCoRau1otr/NL4BZocatRydgpn9an2kza3O\nMc/tQLMKc4dAeqm29voMcrmrtjiaXFQjqd5Z9ZpOC8pZdTylL/DG6e+B23zALyGD2ejvQwZic8CA\nW3nl7PFbWs+8i/d6I8TMFh4aiNX8jTpBe6dCVHTTlgUCvV0udNxYDHpc/Tp+M6TSecZ1HZ7brmG2\n21nfzjKm75MU7Hx/00TphTBPfvkC//yXPyAZqTKklzEH3HzqAYH/+B9myY9Y+c/zBcJtkc/MQUMW\nWF3bZMhipKMz4jUJmPI72P2T5Js9+qKOa4kKw+0MecHMnW6FTdMkv2XdpdEDk0bmRx0vk5Q5WXOw\n6LMTJMtwM4Wo04HdQ7ZUZ0zX5Xl8PFp5gYR5HGl8DqkQJ+9bwl6IEjP5GTVpsNWzvKCdxmg0cMxe\nRyxm2MBJkBzP1p182tvAvnOZTWWIzqFHEDstQvV1xOEJskMzmEx6Tsbq2PceJZ/NY3fYEffcCWe/\nC/uOqwLuujAs51SROR4Ao0UVkBoR7G7VgJOJQehOVWher4+slSB4BFy+GwI0kiqTz+TVtlUuFyJ9\naDfUa0RJ3dtq4ebc9UG0csAHkEHN5vuQQRp9wIBbeTuntfzCJr+8RCRVRsnGkfMpBLubhGKiVyky\nNjvLfD+LJCg0Ujs8HZe598QdxKMxAvsWaexEeWarTrbUwNIoYLP4+cY3VyivxskmmngmrBy9L8iw\nT8eMV2GuFebf6j4Os4swuZd+u8GCtorkHuW7CQ1H9DmeWCkw6rAy0YrTtHooVFv4lRIx2YQsarlW\nFrjd1uACPrydDLPBafbMTSA9/U9c6zuI9ax00CBNzNPZ3UCyO7lTmyHRkAkFJyEX59rGDmdaQyh9\nGNY0GLdJiEMeQkcOsbISIV6Vibe1WOtJ+vtOoDSqBG0CB6PfA72RhizwdNXBWL9E0KIQrfbBZCWu\ndeK1GZlvR5F8AbqSnrDggegyoTEzjY0rxKbuJtTcgns/CUarugGXn1EfO00opuGuj9+YTx5ZXkOx\nuZAjl5m9406kUurWdPZr1FNuJMtq26p2l9hWnNDc+KtnrQ8Y8AFnULP5PmQgNgd8EHmnainfzjW9\n1vGnrsQZ75eot7tczvfxtVO49h6k29dg0QmECpdh6Ti0m0ReOMWm4ELXltk6E2Z7q03kcop8vsvQ\nmB7z3DD26SH8ExIH50a41jFzxKkwMyTx/LUUSnQZ/z0PMFbdIjF+G6HwD8AXpJGI8XRBzzg1pEP3\n4Tn1z5yWZtELCpPVTerBuzh/foV93RhnPHciZLcZd1m435zHNBWAzK4qssw9LjbN7I7uR9OocGhI\n5lzbwcPNC0h7jsJz/86G/y7YuECr0aCn0YLBTFp0cvz+23lmJcUDI10ql1/k79oh5r12pIk5Jqtb\nzJfXkMwWNTLYrNNVFMI7eeYaW2y59xKQUzQEPTHzJKFJNzjHQKdnrWHEf+UbRL1HCZbWkJxudWJT\nPqmadPIJmN6n1mPKXTVa+ZLJZ2M3T4AcDaefWKH16rGlr1FPuRYvqm2rTp0keM9xpL4yaEs0YMAr\nGIjN9yEDsTngg8gvZH75zyJa36Dv4avWpC3dHE3oHGMj3yawFKLRE3h+LcnksI1T11L8yrhCuGPi\n8ukzjIz7aChQFCz81okQxnSE3ZqZ5R9d5PnLVWIXd6nWe0zvHWFuuMntIT3bWi2SzcFke5eo6MHu\nHOLFgpbfD/bA5SXWd6C98jRy6A48uWuc1gWYM3RIbG3Ry8QZG9Ij+UNsb0RwaHskEmlmpyc4v1Ni\nSV9lS7GRayo4DQK5WpffPuABq40YTkIOAZZPslbT4p8eZ0MeYjeR5Y7Di5w+v8qJORumSgb6MhRz\nrCXLtGpVtpQhxqhQ0NoYN/eJO4IoRjsTZtjJ17lr1soL4SInNFt0h6eI1QVCTi1sXYXgIShmoFGB\nPqzVRPzFNaKjhwnaFKSZJYivw7GP003vEC60mBvSIVUzqrjMbIPJBqFjsHke+gLM7VdrLicW1NrN\n9XOslRT8wzai2hGCI1a1v2YhCX2IFNoos/uRRd0t35sbkXA5gzTqH8wsHzDgNRiIzfchA7E54IPI\nzzu//LrI3MpUuO8VLYfekNdppB1Jldl8yUAkCGDQadHk4sguL7OtOM/FW+x0jXQrRdzjPnRaDQ/s\nm+DSVo54tsKUWKWvQLRvJR/OYdhOculqje3lJDqhx96gAbffxN0hLZ07H2ShEQbHKOGnvs9cYJJI\nVaBVrdK2ukljRduqMuEb5keXt5m3yKQUM49NCpzdbXBizk48mYel4/hSl7hW05DNVTlRv8ByTYd2\nyEWu0qQtGtkyT1CuNnjEkqfrniRTqHLCmCXqO0JQU0RKRcBk41p4m5hzEW8vy4xJJqZ1o9GZ0JTT\nyDoTgtxG06rTrlTo9gUo59EqHbSiQEiq0NAaedq0jymjjNdhIlFqILRb+C0QTRQIHtqPlN2C8RAR\nwzhKdhe5UmRWW4fUJuGRg8yVVpGGx0FrgLs+CqWMGnVMbcG1M0RkO9u5Gj3XOGP5debtCtLMXqjk\nQKtXU+KCoLrWCym6gdsIX7zEXCOK9OBvqXv+/DfgxK+zsbFNoHqNhntajXq6dbf++Bi4yAcMeF0G\nbvT3IQOD0IAPIm9lfnkkVSZfbZOtNJGVPvO+IarNLplyk2qz87MZgF6nkXa+2ubAtJtMuYkoapBE\nDQFDE5PdTnR5hYrVy+M+he2+Fa1Wi5cqmWSGfqlIJd1h50qFp/7pIt//2zMUryaRzRZOPLaH+z93\nlEc+MU1wr5W7f+NRSr0ewfxltm2zFIf89Jt1SpFr9EUt59oOGJtlurKOLXQI7fYKmKzMTPlYMtT5\ndlzkE+4qBouFrNZBMx4j09Eg1krYa0nsZiNVwch8Pcym0c8dliqnymacSg2XzcxyEfZ4rfzYsMho\nJ0tZFrEvHkZ0jVG0+TiqSWDpt4hJw4RmxymGVwmQxeSb5MpOiaPaNBarmXq1wZKUZ0Sok5ccmCSI\n6nzYuhWmpQbbhQaB5jZOq4FosUvA50Eqp8AXVB3tGisBQwtTt87JdB+GhukbTTitJsSFo7B4p1p/\nqdWpafGNczCzj3xsExsdFv1OLkUzbIseusUMdtqIrRo4R1Szz/ZV6LYRW1U8nQLiRFCNhKa3oNcB\ni5PsygVM+48RvbpGwOdC7LbU8ZZDL/3nOXCRDxjwurwV3aL9RS1mwIABA14PSdS86dS50udGmvuZ\nqwkmPVYEAUSN5meLjqaiatRr5Tm11+LLIlayotDtKei06r0iqTIN54Q6cvKuezjzg0tctE1zNbrD\nEYuR7/zoMlureVJbJSYnjOy9dy8f/R/u4b94ZbIOL3M+F1I2xkamQqCXpCG2iF2+RKgZhfnDKJtx\nAjaBBiW+btnL7RNOPGUZv6GDcOAE565sst9oI7Vd5whJziYa/Pq9h5HOnITkBrOFNGtjRxEnQywU\nLoDVQDhVIliJIBkNjI2Z2Cy3cHaKfOyIn//nTA6hUyDeLxPqr6Pdfwy/WCW8HiUkJ4kxR0scY6eU\n5L7hMixvImOhUS4TvXoNb7tBw2YlmioRVFJqW6H99zG7eoZwz0awG4UhD+G6juCoESnwcfj+lwm5\nvZDNg6ABfwgEDfLaGRqzk0QFF74lDwGXlkY2TbhhJ6TRwulvqaIvvgGWIYiHod1A1g/R0gqsJiv0\n9x3nhLRL1xEknG8SUlLQrMPV51Wn+u66Wtc5NqO+9/U9H52C5ZPM3n2ccL6jpu2HfbB7DXj364YH\nDPhlZZBGf5cZpNEHDPjZeHnqfXrERixTfXMu8zeYRf3K2dnd4UnCmQYOjYZrZ3d4+kfXuHZul1qm\nhmfOxT23udk3DaF5K3HJgSJqkUemb60bTURY6zvxm/tET58iKBaRnMMwMsVaQ49/5Umilll2ekaO\n26v8U30SBJFyPs+9rhb9YhbycXJmL/fu9WKKXFDvW87A9BLsrKntfoY8qqBafUEVV4U0P2p5SFin\naLY77HNpuNo0ctTRZ6hf4R+7C/zn4TS7bR1Bnx2p22H1/AWQ9Hh1XRK6EUJuLdfEUc6sxXFrOowK\nNcRWgwV9BUnSgtxTJ/S4fWoPSrMdXGNqLSZ9yCXANQrVEuhNRHCgmOzIFif++haxyXuYK68S0Xnx\nCxWigpOgVVHrJHsyVLPQ7aqCMbMFTi9dm5s1wxSMTiPQZyZ7mWi+RbCXQpoMQr0MB06oDdtfuefb\nq7B5Wf2sHKM3Heaxq+q/RRDA/7Km7m800/xn4ee9fsCA9zBvRbcMIptvgUQiwV/91V/xxBNP8IUv\nfIHHH3+cz3/+8/zd3/0dRqP6h+5v/uZvePjhh/nmN7/JN77xDURR5NFHH+XjH//4u7z6AQPenwiC\nwMnVJF6n+S1FRlFkVWheN328DEnUsODWk+g4+PF3lgm/eJK1lSKdeoeZA16m93n5o//zY2iGTYT8\nLiRkePpfiOjdbG2n8FlFxgLzhJPlm+tSZGa7SZ4+t4PPIhERRpjtg2QZYjZxnrBtluDR2+mfPMk1\nPAwPmZFEkQO2Dr1Sm7xmiId7p5H8k9AowoH74IUnYXY/xDdVgemdJiLbULaWkbWjzBosSGIGJB3H\ngh6ykTDP5uzMk8ZT3uSb2iV+dzpMQpwk6Dcg2Zzw7L8iCDpks4NTioXx1Ardw59Ec/ppbjf08FW2\nuCbb0QoKUq8NvbYacRzzqo+zByC6DKkYjM1COgKh28Fkh3Pfg5FJlLJCoLFFo7JJzLWHUOS74Jli\nlgLhoUWCi/NIz30dNAKUsmAdUh/TMbWpukZA0ulY8tlAKNLt9gj37QTnPEgdt9q0/fBDqjno5SLv\n+p7n4jB3QJ0mFN9Q6zHH51XR+Vq1mYpy84fJ9XPf1Hft57x+wIBfMgaRzbdAu91GFEUeeugh/uiP\n/ojHHnuMP//zP+fEiRMcP378xnmVSoWDBw9y7do1BEFgz549vPjii9jt9hvnDCKbAwb8bLyRg/0n\nK3FkBbo9meOLXky61/gd/QrThywrRFfTLL8YY/mFGMunNhH1ElMBO3c+vIRu0olv1s30iI1IuoJW\nI6ARQKPRIGd3me3E2drJII8F8MzMcvryJg+fuO1mZFPuwrNfZ8O+QKCXZKWkEDdP4k9dYvbgAaSt\nS9Dt0LW4COebyPUavW6Ptn2UdFvDg14wyS1IRsAxrE7Oadbh8tNESjLbooOezkSyCUeGerQzCcR2\nnSVSfJcgLrlCWnJwpzbNdh2uSJMc7O8iSXpmfQ62tW6U3Q3kXh9/K87JkeOcyJ+kO3eEmG0KIRGh\nlYojK320kpY95hbPNezIPYW42c9BTRppMsisUkAS+morok5TjSzKPWjV1X6Y1SJrHRN+fYdoy0DQ\n2EQqZ+DgCW5M/Bl+ad756JTa9uhHX1FrPLO7YHWA3qhGLXOJmyJu5SQs3n2rY/zyM2q0td1Qx0yO\nB9U9V2Q1YtqX1TpM/8Jbn2n+s/DzXj9gwHuYQWTzHUKv17/m61/60pf4q7/6K4aHh/nLv/xLTp8+\nzdzcHFqt+jHv2bOHZ555ho985CPv5HIHDPilQFYUGu3eDQf7rcfggSUf+WqLp5cTfOiQ/5bjkVSZ\ndqtHdKVL6V+f5+rZbVbP7eIctrD36CR3PLzA7/5v97Ma22JXsaBYDDT6MO62EM1UWPCpNaFPLccZ\nd1lodWTWhBGkxiZjuVVOxnOceOT4rSl9UXpJS8k0Mkl2mOYB+w7dhz5COBIjNHcIzv8QKbNDqJDg\n2vhd7HQk5I1VHrQUMCku8M2ApCPiWkLJtZCLOWadPhSPi/G1F/GZ9PxTd5hWs43SB7FRImJy4+tm\n+V53knucfc7kbYxrqhwxlgnVUjSwEa660MopAiN2GrUKMdte/Lsv0t13lGhHT7CdAGOHNb0e0e5m\nobWFpBWRzU4e0O5yppVjd/Qg91avEta6CS3MwPNPgH9erbE0msG/B5w+OPMtZp1OwppxgsIm0vgc\n2J3gGFP7YxbTag3o2DQgwMZ5sDqJePajyEPI1SKzk7NIuYSanh4ahmsvqvPJV07C3rvVzzoVhUJC\nHSfZa6v3HA+qUUW5qwpAXhKgP038jU2rIvWtCsWf9/oBA37JGIjNt4k//MM/xGKx0O/3+cxnPsOf\n/dmfcezYMaxW641zrFYr6XT6XVzlgAHvX2ZH7YST5dc0A3V7Mvlqi1PXUty/bxyAeqXF1bM7LL8Y\n48zJTeLrWcZnXQzNujh49wi3f2qOgM+E4PYy63MhiRpWqh1+fc8Y5yJZlmMFXthI43WY1DdJRenl\nCvjMZlZ7HURBYDYwRVgZ4mGHhBQ5q6ZpFflGP0cil5kdrROuC4xbKnT3PkR0fZOgkoa1BJFcA8U5\njVzT0o8u8yGxQGPmMLGuh1DpKgy5wTuHsn2NwISbxvgo4fNx4iY7Lc0IK/kOjkaWnlZA1++zYOpw\nUjMEOoVAL8eT6XF+TZek1+sTK7SZNBiIjh8l2N4hUtfSSO8Qtc4RLK1AcJrw4ofQpiJsXXkWeXiK\nBWUDqZYDxwjsv5/4xTxntDZ+3LPz2cI5oi2Z4PGjau1jv6/WaE7tBZ1Odf0n1+GujyN1OoRKaRhe\nVCOXmR2IXlE/18AhkAzQbauzyg+cgK1llI3zBAJ+Gvv2E16PEDqwV625XDsNw35VRI7OqH03x+fV\n1PXMPlXsarWw5xgsn7y5Jz+LyLyOKP18qe+f9/oBA37JGIjNtwltnq58AAAgAElEQVSLxQKodWWf\n/vSn+cIXvsDHPvYxarXajXOq1SpjY2Pv1hIHDHhf80Z1mscXvXz72TD2aod/+Mb3WX5+g/h2hWDI\nxdLdQR7+3FHuuTdAqtFGIwiENAWudIdA6eKXc4STWkLjDro9mcpWmNxOhduHJJb2jtLoCWotpkZh\nbGaap5IFqHaZFMowMUxIq1FTs02I5Fso7RZyPMHshz+NFA8jaSC0OE83sUn4/2fvTYPbOvBrzx+W\ni4UbCIAguIIUsZCgqH2zLNuyLbe7091JJ939kveSvMqbl7zUJDUzeVNT82WmaqpmvmQqk6pUzbd5\n9epVPmR/SV463Z2kF7c3WZZkWRsXcQEIgisIkABBEBfrvZgPf1KU1JKXdtuS7HuqXKJIXODeC5R1\neM7/nP9snEh2EiU4BulFdL1BOOBHXbvJW3gZaDKTSCRFue0blj3cS7NotRZUyw6J9CRWtw99NY6p\nsE2XScXe6sKh2AiV1lAsVur1Bl0uO20WjVRhm7riRK8X6KFA0tlPZOkdlM5+gvYSMdsQkdICytBh\n2M4QnfsX5jYqhA/0MFlo8LrrFIFmneDBgyi1Isc6FZa3Wvl31lv8oNLPKecW8R99j6CiotisEhBK\nL0KjLtVBTS7o2AF1C06+Kp2Zc9dlNtPtF3J48w248Otw4zUpc9fq4PajubtRO0P75Hy+IYHxIy/C\nWuKn5291DTr6YPYanPm62PLeHmN20oCBJwAG2fyE2Bt5vXDhAn/xF3+B3+/n5s2bPPPMMzz77LPM\nz89Tq9UwmUxMTU1x/vz5x3zGBgw8/Wg0GqwuZJm4unh35rKwVWL0kJexI538D78TwPLsOcw00LIp\nAt4WkksJIm6FuMmDur7I0maacy15El1HsJpgbnyKLq3Ou1fmePnMKIubRdTENAl7r5C/tQyKXiO7\nvs6xw2H6vc3EZmawWRrovh603DqN1irRrSnU/CKxdy8RLWRh7FnYyaFEjhG1N8FGVYIvbR604SEm\nL19kiQBdzWXiSjcjnXmU0jYMRIQwOZsIBkPEJmeIHBpjQTUTsKXwRbq4vJDjVX0VpVAGuwN8/XTP\np4hbxkDLYG7oLFTsBOw6Y9UkitMBTjd4ulHUbaKpmMw4bq5A6Djxko0FU55i0cSKqvGqI02te0SU\nxf4OlPYOzjvWSKThVEueaH0d1bJDrDlItK9dZia9vfKc6raQves/hOFT8sZl1yQNvjfb+f6PIHRM\nyGBuHb7y2xI2ymcIHntVlGw2UHx9MrtqsYCtSQhpav5+tXLPun7hXwnh7Q09nJQaMGDgM4dBNn9G\n/NEf/RGJRIK/+Zu/obOzk29961v8wR/8AaFQiFwuxx//8R/jdDr5kz/5E377t38bs9nMH/7hH95V\nQA0YMPDRoWk6yZk041eSXH4rRvzGCpjg8DODHD4zyK/8zjMMDndiTiVEyUqMM5deJOxvRh2OkNyz\nYSslTFOzXNxupsetsOjqY8SSZ8HcRLixgdrRhT1ZpkkvEWy3ENuqETm2a9ubTJjH3+JUcxu+coqL\nVyu8GnGzsKkS7m5DrXTx1q1FBnp8JE4+S2ThTRgYIR5fQu8OoW1UCLY3UACqVShmMbm7ue49wbHT\nR+h//f/jPXMXG7hYLdXxLJvoN/kZZhtl8iLRngiYNDRVpdvv5eJanZdMCyjFIigK8R0zuqUCo88Q\n2MqQ8nXxG/pbzDQ8pK0dLLQfRMNKsL6O0u6D7U0pTvcNyLxjYhy9ZYgXHRkmOk/T2ElSM1lJlBQi\nXe2QWyfY6yZ2e47IgW7iMzFUc4GE0kPEWRVV0uYEZwss3YEWNyRuC9Hs6JGZSa0ObT7ihQb67Qm0\nc79MsK8DJb0ANCB5Ryz557+5r2THUjJOoOuSwM+uQVmFfOb+FPm91vXen8bspAEDTwSMNPpjhpFG\nN/BFw0fZZ16t1Jm7vXpXtZx6f4l2bzNjZwbwRXxceHWENn8rixs791vreyng1RjTmzUCo1ESmyUi\neuburus5cydhNlFd3SRnY0SPjjGd2iGwdp2Ee5TI2lWUdEJs4HaflIRnU7C+yHS2RvfhY1y8Ns1L\nnhJNp15hOpEikJ0i0fBwYHOSJC5CLpNcly/AnL2PsMeGevsiyViSqNsiNrKrg7m0iradw2epcHml\nzJC2jtXdSVktUS+XoNWNo1IgeniU+HKGRWsnZbMNLZvmZcsiTT0DMPse9I/wetpMXz1L0daKpVpC\ncXcSsBZJLKcxt3mIDg+gLi9wqfUIAwuX0CoVgo0NlA6/EHRbE9MNL4HcDIlsmQOdzST1dkItOsp2\nClq9sts8eBQGRqi9+XfE2kcJtZtRFsdh4BCgy+zl9hZYzKJg/vL/KEqjVQFMUCsz9957hMMHUHUT\nSUsH0e42qSQCGDkjXaJ7hPH9H4nlXq/BdgYwS3q9zQvzt+D4l36+vZZGR6YBAx8IYzf6UwiDbBr4\nouFhFUbqTuVumGfiapK522v0Bb2MnR7g0JkBDp4K4PaJK/CBe9XvqTeqYSG2lifU7WJxPYeeXkbz\n9FDHzFBHE4kr7xA50IeST1FzdRGLLRBq0VDMJojfID7yVfRyGW19geBAN0qjTs3eQmw6JmSSBri8\n1BomYtsmQi+8iFKvwqV/kNCLLwDTV4TAZadJ7JiI9LhQGnVwdRC/Oc586xDunVVW52K8MmBnMbVF\nWYdrzlG8rRasO3kuKEmaOrqY6zkNlRK9bDOzmseRWyVq25Z6IKuVH9UCnLNvcufUb2BL3GCkskxM\nayMU6CRetBAwF0jYuzE7W7Fd/yFqq4+VmpMD5UXMvWE0/wG0tg5WllP05OcZPvc8yvz7YkWPPku8\naEZPTKKZLARbGyjU4eDzsDoL4RNgcxJfyqBPvovm6iTY34HS1gEr00IWff1C3tKLTM8uEjj3EonE\nMpHp76MED0HsJjS1gqdL5jL3ytmTU5BZkq+9PfK4U1+W2c8j5+VxH1DY/7Hx83wuAwY+hzDI5lMI\ng2wa+KJheiVHm8nMGz+eIRfbYOq9RZbjm4QPdzN2eoCx0wNET/TR3Op46PE1Tb9LIh+6PehBZSqz\nzFxaJexRUD39xDdKWM0mQo1NFrdr6KlFksUGvcMjmKYvE/yFX0F546+Zaw0RZgMVhaS9h+hQF1z5\nZ7BYhfS0uonfHkcfHENbnCPorKJYkO0+fcNw513QdWrJGWKtQ4T6/CgLNyWxXVWZK9sZ1DaYKChY\nyzscsmap1Ru8poywWrHg97TxTG2WNC1Em8r8uNBBubWDeqVCf7uDw9ZNFIcdFiahqYUJ/NSyGVL+\nMQbVJUz+foILb6O4PNQqFWKai5C9SLxgplYpY25qo65p3G4b49TYAbrXbnCl6uNVRwpVaSZZthLt\n90C7H97/IXPeQ4Rnf4g6eJxkViXqc0BTC2ACGtDcxlxOI1xeRK2bSDr7iLrNUClKAr1agdUYBKLU\npq4SG3iO0M48iqcDOgfllwQaEvJxNovFr+syb5lLQfAY+Pp2/74ulUd7hPTWG+DyyYzr4efv3yL0\ncWF0ZBow8IEwejYNGDDwxKHRaJBazN0N84xfSZLN7HDwZIDDzwzw+//XVwkf7sFm/2j/O/rQ7UEP\nbm8xW9G8PajlVRLvXmQkFEDpHoK1DHq5RNi0SdkdgMlLBHq9xG5NEtXraLk06lCARAEi4QGYuQKj\nZ+DSd2VbjmJBrzYTDg+iZqaIKX6iPW6I39q3jHdyKGhEN8ehvES8YkdXutEsUK9nqeVy2JoPELEV\nYKuM4usnWCww6NDw1TNcrnTy6oAVTG30NnTqpjxlXcW2toNy8ohYz+0+sNoZLqwT649iVctY/X0U\nK3WmHYMcKi6h+AewpYsslB3UTRqLrSGe27nJu44ox/pa8FHgoiXIQKcZ9fgFEpffIWJOgLVLqoQa\nDbSNFKrFQWJ9i4hlG1JVsdRzaegLQYsHbXsLFRuJvhNEMjegYJPgUrUCrg7IroPZjOLpJDr9fTj3\nK7C9ISqlVpPRBbNJZjHXF+V9rJalj9PdBZe/C+GT0DkgpB/2f7nIrsHwyf0qpJ8VxpynAQM/dxhk\n04ABAz9X6LrOwkyaiSuLd23xRgPGTgcYOz3AN/67MwwMd2L5qDvNPw5SCUgnZYMMELf3oKdXqLc4\niU9MMnL0MIpeFfWqL4J2/b+hel0sJVY4d+FFEpO3iYSbiHtfpZGY4D/d2sFd2uD99yc5HPBw0KNJ\nMtrhgM00mtmO+uO/IlG0EhlUiU+n0btH0GIrBHdiKMWcEC27A1weFld0+ix1yhspGlaFpMlLRI2j\n2B2ybSe/ieY/Rnf6DhedJ3nJPImS1aE7hElNEWxXSLR5iShmGH9bNuGsL8HmKopZIdpZYLlgo5re\nYLFsx6K0MaJpKJll9FoTYS2D2uShUU6x6vDT56jTv3CRi/4Xeak5i1LXiV27QiQUQDnwZXjnO/DS\nr8Frf06wsEWs1EXEvCWKsrlJNhrt5CQhvjxL8PlvE/uXfyTS1kDJWWRus8UtdvTmCmRWoaUNNlfh\nl35f9pV39oNVIZ7X0Yt1tESKYP8oyr2rJodPwsx7Qm77h++vMkovibja1A53Lsvayk8CoyPTgIGf\nOwyyacCAgU+EWrXO3PiarHy8mmTq2hKt7U7Gzgxw4nyI3/pfX6Zn0IPJZPr0T0bX4dDzkmq2WNBN\nVsKHoqiLMZKtLpTuAViekVWGFoXg4cPE4ou8eGyI5OU3ibToKFoPemKS6PPnWbg4iaXYxolwmItv\nv4c9eYtoUxVsdrBYMJm7uWgeoudAG8R/jN48QLizCdXtIPb9N4k2UlLu3uoBq0K9yU5vLcadcglb\nOUO0tQIVK1gd0N8PpRJBR4WY2cWrzWmUrU2w2CCdINjlI7ZtkX3gyxmx8ptcoI4Dcj2sxelujZJo\nDDCgJRkM+YmNtxP12NE2aqgmhUTRjI0yZosdbXCMxe0MrzZmUFY3oFYhalEg3QzbaahXYTsH2zmU\n7iGiyirMJ6RMvVGTneSBYXj3HyF4FOXSPxDtboWlSakp8nSDsw0WJuD4K3CgAlPvyqzl9R/JNdgc\nYDKjt7oJ+1tQ27qITUwTPTIqhPLw86JWHnv5Ef2adYicgttvgH/AUCMNGHgCYcxsPmYYM5sGnjaU\nihXuvL981xKfu71Kz6BH5i3PDDB2OoCns/XDn+jTwL3zdhYr05kKAZeVhMmDdXMFs66hmSDY2YZi\ntYgSGj4OV/8Jnv+WHJ+cYrruJkCWv1q0MdxUYr1kZsC8zeHTJ1Fu/wR2NqEvwly2TnjQjzo3RdJ/\nGNNOjsDICBfjWfozE5gsCkFzVoJDgSgTRQc1WzPm9QSj1SRKzwFJeHf2S62PpktxeXZNaoKqFTBZ\npPqnWgYakva2NYl6W8gRx43u6kArlQiWlqD7AK8V3Jxt2WK5YifSVEVpaaOWWiJWbyVUX+MiA/S5\nHRSLJSwmONTegFpZ7OudPJgkLU+rWwI6tmYh8lspIYf9UUgnZH95qQjebpmnfO9fhMg3tUNxCw6f\nh/QCFLclRBS7DgOjQj5dPiGHAwdB3Wb6B98n8NJXSMQWiBweRbE9ZC3wA/vtAVicgnpd7Pf+D9l5\nbsCAgU8MIyD0FMIgmwaedGxtFpm8um+JL85tEBrruhvmGT3ZT3Pbw8M8nznuJSPri9T8B4gtpgmZ\nt1iw+CTwU4dkYoVouE8I28IUuLwwehamLoOni9r4RWLOQXoS7/A2B+hVlxhz6UJQHc0y17exwrQ9\nQGAnSYJ2IkcOQXaNWGIVgGh1CXWnKOGikUEJxmysEZtfJVRfRylvw4GDEvCp1aCYA7NVKn0aOmys\ngqMJmlugkBcr3tkmtnVjl3TWKszpbsL1VSYbHayYOwi0NAg06SSd/YT0NEq5AIoNaIC9GVIL/Fkl\nxJH6CnGTlwF/O8e8SAp8/iY0zNCyu3s+uyr3RtOgXBSVtqHDTlae68Bh6OyD5TlRQUsFOPkVuP6a\nhHx0TbYrmS2i7ja3iTr59d+V57r0D/IcGyvURs4Qm10gNDYiRPNRFUQPfh9+moAaMGDgU4NBNp9C\nGGTTwJOERqPB+vKWhHl2bfHN9QKjJ/vvksvhIz3YHE/AP+qphMzr6XUhSr1hIRt7ZCSVkM7GjWXo\nDUmXZkcriak7spWmxSW7s7/2H4ivbqGPv4nWHSGYnZK5zvBxuPZD6Xcs5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x6X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\nnpHPJhjf9+1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P57U0VrLDvrnaU7n1zC0tvg3ocFw9eP2q38sUOsPnSxf5fLkOEVk0FDZF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\nCRiFTREREREJGIVNEREREQkYhU0RERERCRi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      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}